Find Academic Supervisors

• Human-Computer Interaction
• Interaction Design
• Cultural Probes
• +6 more

Andy Boucher is a Lecturer in the Department of Design at Goldsmiths, University of London, Faculty of Arts & Humanities. He is a core member of the Interaction Research Studio, where he has been actively involved in numerous design research projects focusing on human-computer interaction, particularly through cultural probes, ludic design, and environmental interaction. His research interests span multiple domains within interaction design. Boucher has made significant contributions to the development of cultural probe methodologies, creating tools like ProbeTools and TaskCam that enable novel forms of user research. His work often explores the intersection of technology and everyday environments, particularly focusing on how people interact with nature through technological means, as evidenced by the My Naturewatch project. He has also contributed to research on domestic technologies, energy awareness systems like Energy Babble, and technologies for elderly care settings such as The Photostroller. Boucher's publication record shows a consistent trajectory of research in human-centered design, with a particular emphasis on practical, tangible implementations rather than purely theoretical work. His recent work (2017-2021) has increasingly focused on nature-technology interactions and community engagement through DIY approaches, while earlier work (2007-2015) explored more foundational aspects of ludic design, cultural probes, and domestic technologies. His research consistently demonstrates a commitment to designing for real-world contexts through field studies and community engagement. Boucher has been involved in numerous collaborative projects with William Gaver and other colleagues at Goldsmiths. He has contributed to several notable design projects including Datacatcher (a system for batch deployment of location-aware devices), Energy Babble (an energy awareness system), and The Prayer Companion (a technology exploring spiritual practices). His work has been exhibited internationally at venues including the Museum of Modern Art (MoMA) in New York, the Victoria & Albert Museum in London, and the National Media Museum in Bradford. As a design researcher, Boucher has focused on creating practical tools and systems that can be deployed in real-world settings. His approach emphasizes hands-on making and prototyping, often involving batch production of devices for field studies. He has developed expertise in creating research tools that facilitate novel forms of user engagement and data collection, particularly through camera-based systems and environmental sensors.

• Cloud Computing
• Serverless Computing
• Distributed Systems
• +5 more

Ass.-Prof. Dr. Sashko Ristov is an Assistant Professor at the Department of Computer Science, University of Innsbruck. His research focuses on serverless computing, distributed systems, and workflow orchestration, with applications in cloud computing, healthcare informatics, and high-performance computing. He leads research on federated serverless infrastructures, resilient function choreographies, and digital twins in construction engineering. His work addresses challenges in cross-cloud resource management, workflow scheduling, and anomaly detection in microservices. Recent contributions include frameworks like StoreLess for federated storage orchestration and CODE for cross-platform serverless deployment. Research trends in his publications emphasize bi-objective optimization for batch workflows, GPU-accelerated document processing, and disaster-resilient cloud systems. He actively participates in workflows community summits to advance scientific workflow standards and interoperability. Dr. Ristov has collaborated on projects like the Montage workflow analysis and ECG monitoring systems using serverless architectures. His work bridges theoretical computing models with practical implementations in healthcare and civil engineering domains.

Teng ZhangView profile

Associate Professor

• Statistics
• Machine Learning
• Signal Processing
• +7 more

Dr. Teng Zhang is an Associate Professor in the Department of Mathematics at the College of Sciences, University of Central Florida. His research focuses on statistics, machine learning, and signal processing, with a strong emphasis on robust covariance estimation, matrix analysis, and phase retrieval. Robust statistical methods Machine learning algorithms Signal processing for complex systems Optimization in non-convex models Applications in cybersecurity and bioinformatics Recent publications highlight his work on low-rank matrix models, adversarial robustness in deep learning, and secure distributed estimation for cyber-physical systems. His research spans theoretical guarantees and practical implementations, with applications in computer vision, network security, and biomedical data analysis.

Winston V. SchoenfeldView profile

Professor

• Optics and Photonics
• Semiconductor Materials
• Photovoltaics
• +5 more

Dr. Winston V. Schoenfeld is a Professor of Optics, Materials Science and Engineering (MSE), and Electrical and Computer Engineering (ECE) at the University of Central Florida (UCF), where he also serves as Vice President for Research and Innovation. He holds a Ph.D. in Materials Science from the University of California, Santa Barbara (2000), and M.S. and B.S. degrees in Materials Science and Engineering from the University of Florida. His research focuses on photovoltaics, epitaxial growth of oxide semiconductors, light-emitting diodes (LEDs), quantum dots, electron-beam nanolithography, quantum networks, nanowire device modeling, and solid-state nuclear material detection. He has secured over $24 million in research funding and authored/co-authored 140+ peer-reviewed publications. Key achievements include founding Medical Lighting Solutions, Inc., leading the c-Si Photovoltaic Manufacturing Consortium (PVMC), and receiving the SPIE Fellow distinction. His work emphasizes interdisciplinary innovation in optics, photonics, and materials science. Education: Ph.D., Materials Science, UCSB, 2000 M.S. & B.S., Materials Science and Engineering, University of Florida Awards: SPIE Fellow 2017 Promotion to Full Professor 2016 Research Incentive Award 2010 UCF Teaching Incentive Award Grants & Funding: Over $24M secured for research, including leadership in the $10M PVMC consortium. Labs/Teams: Director of the NANOPHOTONICS DEVICE GROUP , focusing on advanced optoelectronic devices and materials.

• Probability and Statistics
• Mathematics of Finance
• Stochastic Processes
• +5 more

Dr. Ionut Florescu is a Research Professor in Financial Engineering at Stevens Institute of Technology, School of Business. He leads the Hanlon Financial Systems Lab and directs the Financial Technology and Analytics program. His expertise spans stochastic processes, high-frequency finance, reinforcement learning, and mathematical finance. Florescu holds a Ph.D. in Statistics from Purdue University (2004), and academic roles at Stevens since 2005 include Assistant Professor, Research Associate Professor, and current Research Professor. He advises numerous PhD students in Financial Engineering and Mathematical Sciences, focusing on topics like deep learning in finance and algorithmic trading. Education: Bachelor's in Mathematics, University of Bucharest (1996) Master's in Stochastic Processes, University of Bucharest (1997) Master's in Computational Finance, Purdue University (2001) Ph.D. in Statistics, Purdue University (2004) Research Interests: Stochastic processes in finance, high-frequency trading, volatility modeling, reinforcement learning applications, and interdisciplinary projects in geophysics and biomedical engineering. His work emphasizes mathematical modeling across diverse domains like cryptography and ocean studies. Recent Articles Trends: Focus on algorithmic trading, liquidity analysis, and applications of machine learning in finance. Key areas include counterfactual explanations for credit ratings, batch auction market behavior, and multidimensional risk modeling. Awards: I.W. Burr Award for Thesis Excellence (2005) Nvidia GPU Infrastructure Grant (2017) CME Foundation Grants (Totaling $185K) ERASMUS+ Mobility Award (2022) Advising & Grants: Supervised over 30 PhD/Masters students. Secured NSF, CME, and industry grants totaling over $1M, including leadership in the NSF-CRAFT Center for financial technologies. Active in organizing conferences on high-frequency data modeling. Labs & Teams: Director of the Hanlon Financial Systems Lab, a hub for fintech innovation. Collaborates with industry partners like UBS and CAPCO on projects like corporate credit rating systems and market simulation platforms (e.g., SHIFT).

• Process Engineering
• Desalination Technologies
• Wastewater Treatment
• +3 more

Professor Iqbal Mujtaba is a Professor of Computational Process Engineering and Associate Dean (Learning & Teaching) at the University of Bradford. He holds prestigious fellowships from the Royal Academy of Engineering (FREng) and the Institution of Chemical Engineers (FIChemE), and is a Chartered Chemical Engineer. He is also an honorary professor at East China University of Science & Technology (ECUST), the University of Jordan, and Bangladesh University of Engineering & Technology (BUET). His research focuses on desalination, wastewater treatment, dynamic modeling of chemical processes, and the water-energy-food nexus. He has supervised over 40 PhD students and authored/co-authored multiple books on process engineering and water management, including Batch Distillation: Design & Operation and Desalination Technologies: Design and Operation . Education: BSc and MSc in Chemical Engineering from BUET (1983–1984), PhD from Imperial College London (1989). Key Roles: Former Head of the School of Engineering (2016–2018), Editor-in-Chief and Editorial Board member for journals such as Desalination and Processes . Collaborations: Over 40 countries, including the UK, China, Qatar, India, and Bangladesh, with funded workshops on water-energy security and the food-water-energy nexus. His research emphasizes industrial collaborations, knowledge transfer, and sustainable technologies. He has published over 420 technical papers and secured grants from bodies like the Royal Society and British Council. His work bridges academia and industry, focusing on impactful solutions for global challenges in energy, water, and environmental sustainability.

• Biorefining
• Bioenergy
• Biochemicals
• +3 more

Shane McIntosh is a Research Fellow in the Faculty of Science and Engineering at Southern Cross University (SCU), and Research Engagement Manager in the Office of the Senior Deputy Vice Chancellor. He holds a PhD from SCU and has over 20 years of research experience in biorefining, bioenergy, and waste valorisation. His work focuses on developing sustainable solutions for nutrient recovery, recycling, and converting organic waste into valuable products through chemothermal processing. He collaborates closely with industry partners to advance circular economy principles and improve sustainability. Research interests include biorefining systems, bioenergy production, biochemical development, and the application of waste-derived materials in agriculture. His projects emphasize the conversion of organic waste (domestic and industrial) into chemicals, energy, and nutrients for plant production systems. Key areas of exploration include hydrothermal carbonisation, struvite production from dairy waste, and phosphorus recovery from industrial byproducts. Publications highlight advancements in waste-to-energy technologies, geothermal energy potential, and the agronomic benefits of hydrochars. His work bridges academic research with industrial applications, addressing global challenges in waste management and renewable resource utilization. Supervision focuses on master’s and PhD students exploring waste processing for high-value commodities and carbon capture via enhanced weathering techniques. Collaborations with industry partners drive applied research in sustainability, emphasizing practical, scalable solutions. His contributions to biofuel production, nutrient cycling, and eco-friendly waste management underscore a commitment to interdisciplinary innovation in environmental science and engineering.

Mark PintoView profile

Associate Professor

• Process Systems Modeling
• Chemical Engineering
• Transport Phenomena
• +3 more

Mark Pinto is a Research Associate Professor at the University of Cincinnati (UC) since 2024. His expertise lies in Process Systems Modeling, focusing on developing lumped and distributed parameter models for products and processes using conservation laws and transport phenomena. He holds a Ph.D. in Chemical Engineering from Imperial College London (2008) and has extensive industry experience, including roles at Johnson Matthey, Procter & Gamble, and Pfizer. His work emphasizes modeling fluidized bed drying, spray drying, and polymerization processes, with applications in pharmaceutical and material sciences. Research Interests: Process Systems Modeling Transport Phenomena Scale-Up and Process Optimization Computational Fluid Dynamics (CFD) Population Balance Models Recent Publications: His work spans advanced modeling techniques for spray dryers, fluidized bed systems, and pharmaceutical processes, with a focus on in silico design and surrogate models for efficient process development. Affiliations: His current role includes affiliation with the UC Sim Center, part of the College of Engineering and Applied Science at UC. Previous roles involved engineering and scientific leadership in global firms, bridging academia and industrial applications.

Junfeng WenView profile

Assistant Professor

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

Junfeng Wen is an Assistant Professor at the School of Computer Science, Carleton University. He holds a Ph.D. in Statistical Machine Learning from the University of Alberta, where he worked with Prof. Dale Schuurmans and Prof. Russ Greiner. His research focuses on foundational machine learning algorithms, including reinforcement learning, transfer learning, federated learning, and domain adaptation. He teaches courses such as Reinforcement Learning and Machine Learning at Carleton University. Education: Ph.D. in Statistical Machine Learning (2013–2020), University of Alberta M.Sc. in Computing Science (2011–2013), University of Alberta Bachelor of Engineering (with Honors, 2007–2011), College of Computer Science and Technology, CKC College, Zhejiang University Research Interests: Dr. Wen’s work emphasizes advancing theoretical understanding and practical applications of machine learning. Key areas include federated learning frameworks for privacy preservation, reinforcement learning for decision-making systems, and multi-source domain adaptation techniques. His research bridges algorithmic innovation with real-world challenges in distributed systems and biological modeling. Publications: His recent articles explore federated learning optimization, decentralized collaboration strategies, and reinforcement learning applications in behavioral studies. Notable contributions include domain aggregation networks for cross-domain adaptation and proxy model-sharing techniques in decentralized learning. The work spans theoretical advancements and practical implementations, often addressing scalability and privacy concerns in distributed environments. Professional Contributions: Conference reviewer for NeurIPS, ICML, ICLR, AAAI, IJCAI, and ACML Journal reviewer for JMLR, Machine Learning Journal (Editorial Board member), and TPAMI Labs and Projects: He contributes to open-source projects like Domain Aggregation Networks (DARN) for multi-source domain adaptation and Frank-Wolfe for Covariate Shift . These repositories reflect his commitment to reproducible research and community engagement.

Brian FreelandView profile

Associate Professor

• Bioprocess Engineering
• Nanotechnology
• Functional Foods
• +3 more

Assoc. Prof Brian Freeland is a key academic in the School of Biotechnology at Dublin City University (DCU) , specializing in Bioprocess Engineering . He completed his PhD in 2020 focusing on process control and optimization in nanomaterials. Mechatronic Engineering (2005) MSc in Mechanical with Bioprocessing Engineering (2009) PhD (2020) His research integrates Process Analytical Technology (PAT) with bioprocessing and nanofabrication, utilizing tools like: On-line spectroscopy Biocalorimetry Gas analysis Soft-sensors and ANNs Key projects include: Grain-4-Lab (Co-Lead) - Circular bioeconomy SERKEL.bio (Lead) Principal Investigator roles at Life Science Institute , Irish Water Institute , and Advanced Processing Technology Research Centre Recent publications focus on: Silver nano-inks for antimicrobial food packaging Laser ablation synthesis in solution (LASiS) Nano-silver colloids under laminar flow Calorimetry-based bioprocess control Functionalization of silicon nanoparticles Teaching interests span bioprocess control , automation , and PAT tools , coordinating modules like Bioreactor Design and Transport Processes . Supervises PhD students in yeast bioprocessing and nanomaterials .

Fnu SuyaView profile

Assistant Professor

• Trustworthy Machine Learning
• Machine Learning for Security
• Adversarial Attacks
• +5 more

Fnu Suya is an Assistant Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. Previously, they held an MC2 Postdoctoral Fellowship at the University of Maryland, College Park. They received a Ph.D. in Computer Science from the University of Virginia, advised by Prof. David Evans and Prof. Yuan Tian. Their research focuses on trustworthy machine learning and machine learning for security, with emphasis on adversarial attacks, poisoning attacks, and model robustness. Education includes a BEng in Electrical Engineering from China Agricultural University (2014) and MS/PhD degrees from the University of Virginia (2022/2023). Key research areas include adversarial examples, black-box attacks, poisoning defenses, and model compression security. Notable contributions include work on hybrid batch attacks, stealthy backdoors via compression, and subpopulation poisoning susceptibility. Publications span top venues like NeurIPS, CVPR, IEEE S&P, and Usenix Security, with a focus on advancing understanding of security vulnerabilities in machine learning systems. Awards include the MC2 Postdoctoral Fellowship and multiple Outstanding Reviewer recognitions. Mentoring includes guiding students at Cornell, Northeastern, and Columbia, among others. Teaching experience includes courses on learning theory, cryptography, and programming. Awards include MC2 Postdoctoral Fellowship, ICLR/NeurIPS Outstanding Reviewer, NSF Travel Grant, and academic scholarships. Professional service includes roles as PC member for IEEE S&P, ICML, NeurIPS, and journal reviewing for Artificial Intelligence.

Ingo SteinwartView profile

Professor

• Statistical Learning Theory
• Kernel-based learning algorithms
• Cluster Analysis
• +6 more

Ingo Steinwart is a Full Professor and Head of the Institute for Stochastics and Applications at the University of Stuttgart, within the Faculty of Mathematics and Physics. He holds a Chair for Stochastics and has held academic positions since 2010, including roles at Los Alamos National Laboratory and the University of California, Santa Cruz. His research focuses on statistical learning theory, kernel-based methods, cluster analysis, and neural networks, with significant contributions to the theoretical foundations of machine learning. Education: He earned a Doctorate (Dr. rer. nat.) in Mathematics from Friedrich-Schiller University, Jena (2000) and a Diploma in Mathematics from Carl-von-Ossietzky University, Oldenburg (1997). Research Interests: Steinwart's work emphasizes rigorous mathematical analysis of learning algorithms, including kernel methods, support vector machines (SVMs), and density estimation. His research explores topics like the capacity of function classes, generalization bounds, and applications to large-scale data. Notable contributions include advancements in SVM theory, reproducing kernel Hilbert spaces, and cluster analysis techniques. Publications: His work spans foundational papers on SVMs, kernel methods, and learning theory, with a focus on theoretical guarantees and algorithmic efficiency. Recent contributions address topics like adaptive learning rates, neural network initialization, and the theoretical limits of kernel-based methods. Grants & Editorial Roles: Steinwart serves as Associate Editor for journals such as the Journal of Complexity and the Annals of Statistics. He has organized conferences like COLT and contributed to software tools like liquidSVM for SVM implementation. Labs & Software: He leads development of software packages like liquidSVM (for SVMs) and liquidCluster (for cluster analysis), emphasizing computational efficiency and automated hyperparameter selection.

Edmond MagnerView profile

Professor

• Electrochemistry
• Bioelectrochemistry
• Biocatalysis
• +5 more

Edmond Magner is a Professor at the University of Limerick, affiliated with the Department of Chemical Sciences, Bernal Institute, and SSPC – the SFI Research Centre for Pharmaceuticals. His academic career includes roles such as Director of the Materials and Surface Science Institute (2003–2010) and industry experience as a Scientist/Senior Scientist at MediSense, Inc. (1994–1997). He holds a PhD in Electrochemical and Photochemical Studies of Cytochrome c and Hemoglobin from the University of Rochester (1988), following MSc and BSc degrees from the same institution and University College Cork. Research interests span electrochemistry, bioelectrochemistry, biocatalysis, biofuel cells, and enzyme immobilization technologies. His teaching focuses on physical chemistry, environmental chemistry, and environmental science. Magner is an active member of professional bodies including the International Society of Electrochemistry and the Irish Society of Chemistry. His work contributes to UN Sustainable Development Goals related to clean energy and environmental sustainability. Over 146 peer-reviewed publications and 8 professional activities highlight his research in nanoporous materials, enzymatic biofuel cells, and electrochemical sensor development. His recent work emphasizes flexible biosensors, MOF-based catalytic systems, and sustainable enzymatic reactors.

Yen-Han LinView profile

Professor

• Yeast Physiology
• Metabolic Pathway Analysis
• Applied Bioinformatics
• +3 more

Yen-Han Lin is a Professor in the Department of Chemical and Biological Engineering at the University of Saskatchewan, specializing in biochemical engineering with emphasis on fermentation biotechnology and bioethanol production systems. His academic credentials include: B.Sc. in Chemical Engineering from National Taiwan Institute of Technology (1984) Ph.D. in Chemical Engineering from Rensselaer Polytechnic Institute (1995) Dr. Lin's research program integrates multiple cutting-edge domains: Yeast Physiology & Metabolic Engineering : Deciphering Saccharomyces cerevisiae metabolic pathways under redox-controlled very-high-gravity fermentation conditions Applied Bioinformatics : Developing computational models for protein-protein interaction networks and genome-scale metabolic reconstructions Bioprocess Innovation : Designing novel fermentation control systems using redox potential and dissolved CO 2 as key operational parameters Analysis of his 15 most recent publications reveals a sustained research trajectory focused on precision control of fermentation microenvironments. His work consistently bridges fundamental microbial physiology with industrial-scale process optimization, particularly in bioethanol production where redox potential manipulation significantly enhances yield and efficiency. This systems approach demonstrates increasing integration of sensor technology, kinetic modeling, and metabolic engineering principles across his publication history.

Michael GrossniklausView profile

Professor

• Data Management
• Information Systems
• Stream Processing
• +3 more

Michael Grossniklaus is Professor for Databases and Information Systems at University of Konstanz's Department of Computer and Information Science. His research develops data management and processing technologies supporting data science applications, with emphasis on stream processing, context-aware systems, and web engineering solutions. Key research areas: Continuous query processing for streaming data Context-aware mobile data management Machine learning for database optimization Multi-channel web information systems Semantic web and RDF stream processing Publications demonstrate sustained contributions to database query processing, with recent work focusing on cardinality estimation using geometric deep learning and cloud database optimization. Developed C-SPARQL language extension for continuous RDF stream queries.