Find Academic Supervisors

• Pharmaceutical Crystallization
• Bioprocess Engineering
• Process Modeling
• +7 more

Professor Brian Glennon is a distinguished academic at the University College Dublin (UCD), affiliated with the School of Chemical and Bioprocess Engineering. He holds the rank of Professor and has served in various roles, including Lecturer (1995–2004), Senior Lecturer (2004–2007), and Associate Professor (2007–2014) at UCD. His research focuses on bioprocess scale-up, pharmaceutical crystallization, process modeling, and continuous manufacturing. Glennon has extensive experience in academic leadership, including contributions to modularization and semesterization initiatives at UCD. Education: Glennon earned his BE and PhD degrees from University College Dublin. Research Interests: His work emphasizes optimizing pharmaceutical manufacturing processes, particularly crystallization, continuous flow chemistry, and computational modeling. He has pioneered methods for solvent swap systems, bubble column evaporators, and hybrid dynamic models for bioprocesses. Over 160 publications, including seminal studies on AAV purification and diastereomeric salt resolution, highlight his expertise in biopharmaceutical and chemical engineering. Grants & Collaborations: Glennon led projects such as the 'Design and Synthesis of Nanoparticles with Novel Cell Targeting Functionalities' (2009–2011) and an internal funding scheme (2016–2019). He collaborates with industry partners like Merck Sharp & Dohme and contributes to professional organizations including AIChE and Engineers Ireland. Teaching & Leadership: A champion of pedagogical innovation, Glennon has restructured curricula and promoted hands-on learning. He supervises PhD students and actively participates in academic governance, holding roles in Engineers Ireland and the European Federation of Chemical Engineering.

Hyebin SongView profile

Assistant Professor

• High-dimensional Statistics
• Statistical Learning
• Statistical Computing
• +4 more

Hyebin Song is an Assistant Professor of Statistics at the Pennsylvania State University since 2020. She holds a PhD in Statistics from the University of Wisconsin-Madison (2020) and a BA in Applied Statistics from Yonsei University (2012). Previously, she worked as a Statistician at the Bank of Korea. Her research focuses on developing statistical methodologies for high-dimensional and complex datasets, with applications in neuroscience, systems biology, and computational protein modeling. Key areas include semi-parametric inference, statistical learning, and computational biology. She has developed influential software tools like PUlasso for high-dimensional variable selection and contributed to advancements in Markov chain autocovariance estimation. Her work has been recognized with the ASA SLDS Student Paper Competition Award (2018). Education: PhD in Statistics (UW-Madison, 2020), BA in Applied Statistics (Yonsei University, 2012). Current affiliations include the Department of Statistics at Penn State and collaborations with computational biology labs. She actively advises PhD students in statistical methodology and applications. Research interests span high-dimensional statistics, shape-constrained inference, and statistical computing, with applications to protein structure analysis and functional genomics. Notable software includes the 'momentLS' package for Markov chain estimation and 'pudms' for deep mutational scanning analysis.

• Mechanical Engineering
• Materials Science
• Uncertainty Quantification
• +5 more

Dr. Anton van Beek is an Assistant Professor in the School of Mechanical and Materials Engineering at University College Dublin. He holds a B.S. from HZ University of Applied Sciences (2013), an M.S. from the University of Michigan-Shanghai Jiao Tong University Joint Institute (2017), and a Ph.D. from Northwestern University (2021). His research focuses on integrating domain knowledge with data-driven methods to address uncertainties in complex system design. Notable areas include uncertainty quantification, Bayesian optimization, metamaterials, and digital twins. He leads the ODDS-Lab and teaches modules such as Automotive Vehicle Dynamics, Mechanics of Fluids, and Applied Dynamics. His recent work emphasizes sustainable materials, multiscale modeling, and AI-driven system design. Education: B.S., Mechanical Engineering, HZ University of Applied Sciences (2013) M.S., Mechanical Engineering, UM-SJTU Joint Institute (2017) Ph.D., Mechanical Engineering, Northwestern University (2021) Research Interests: Data-driven design under uncertainty Metamaterials and multiscale systems Bayesian methods and optimization Applications in sustainable materials and digital twins Teaching: Coordinated modules at UCD, Chang’an-Dublin International College, and Shanghai Ocean University Focus on engineering design, dynamics, and optimization

Sun FerreiraView profile

Assistant Professor

• Food Process Engineering
• Biomass Production
• Bioprocessing Engineering
• +7 more

Sun Ferreira is an Assistant Professor in Food Process Engineering at the Department of Food Science, University of Arkansas. They hold a dual PhD in Food Engineering and Science from Sao Paulo State University (Brazil) and the University of Loughborough (UK), recognized with the Best Dissertation Award. Their work focuses on applying fundamental food and biomaterials science to develop sustainable solutions in food processing, including biomass production, bioprocessing engineering, alternative proteins, and microencapsulation technologies. Education includes a B.Eng. in Food Science, Engineering, and Technology from Sao Paulo State University and the University of British Columbia. With international experience in both academia and industry, Ferreira’s expertise spans hydrocolloids application, emulsification, drying, encapsulation, and process optimization. They lead the Food Process Authority for Arkansas and serve as Director Experiment Station (DREX) and Food Science (FDSC) roles. Research emphasizes sustainable food systems, including innovations in food packaging, mycoprotein production from food waste, and genome-wide studies for crop improvement. Over 25 peer-reviewed articles highlight advancements in microencapsulation techniques (e.g., complex coacervation via atomization/metal membranes), turmeric and ginger oil encapsulation, and plant-based protein optimization. Awards include the Best Dissertation Award for their PhD work. Professional activities include editorial reviews, public service roles, and intellectual property developments. Ferreira actively collaborates across disciplines to bridge academic research with industrial applications, emphasizing empathy and respect in interdisciplinary teamwork.

• Experimental Design
• Computer Experiments
• Statistical Modeling
• +7 more

Qiong Zhang is an Associate Professor in the Department of Mathematical and Statistical Sciences at Clemson University, part of the College of Science. She specializes in experimental design, statistical modeling, and uncertainty quantification with applications in computer experiments and network analysis. Her work bridges information collection methodologies with advanced statistical techniques. Education: Ph.D. in Statistics, University of Wisconsin-Madison (2014) M.A. in Economics (Statistics specialization), Peking University (2009) B.S. in Statistics, Nankai University (2007) Research Focus: Dr. Zhang's research emphasizes optimal design methodologies for experiments, particularly in network A/B testing, Bayesian preference elicitation, and stochastic simulation calibration. She develops algorithms for efficient data collection under covariate and network dependencies, with applications in engineering, healthcare, and social sciences. Key Achievements: Finalist, INFORMS Simulation Society Best Student Paper Award (2017) IISE Conference Best Paper Award in Operations Research (2022) Over 30 peer-reviewed publications in top journals like Technometrics , Biometrika , and ACM TOMACS Advising and Collaboration: Supervises graduate students in experimental design optimization and statistical computing. Active in collaborative projects with industry partners on 3D printing quality assessment and space weather data assimilation.

Kripa SinghView profile

Professor

• Environmental Engineering
• Waste Treatment Biotechnology
• Anaerobic Membrane Bioreactors
• +5 more

Kripa Singh is a Professor in the Department of Civil Engineering at the University of New Brunswick, specializing in environmental engineering and waste treatment biotechnology. His research focuses on anaerobic/aerobic systems for industrial/municipal wastewater treatment, nutrient removal, groundwater remediation, and biofouling control. He teaches courses including Introduction to Environmental Engineering, Wastewater Treatment, and Air Pollution Control. His publications demonstrate expertise in bioreactor design and optimization, particularly anaerobic membrane systems and granular sludge processes. Research themes include: Pilot-scale testing of innovative reactors Modeling hydrodynamics in treatment systems Removal of dyes, heavy metals, and pharmaceuticals Groundwater pollutant remediation He leads major R&D projects like the UNB-ADI Atlantic Innovation Fund initiative developing novel bioreactors for industrial wastewater.

• Mathematical Optimization
• Nonlinear Optimization
• Mixed-Integer Optimal Control
• +3 more

Christian Kirches is a full professor at the Institute for Mathematical Optimization within the Carl-Friedrich-Gauß-Fakultät (Faculty of Mathematics, Technische Universität Braunschweig). His research focuses on nonlinear optimization , mixed-integer optimal control , and robust optimization for dynamic systems. He was awarded the Klaus-Tschira prize (2011) for public science communication and the Hengstberger prize (2014) for junior researchers, and received an ERC Consolidator Grant (2022) for his work on optimization under uncertainty. Alumni of Heidelberg University (Diploma, Doctorate, Habilitation) Former resident associate at Argonne National Laboratory and postdoctoral appointee at the University of Chicago Leader of a junior research group (2013–2017) at Heidelberg University His recent publications highlight advancements in mixed-integer nonlinear programming , real-time control systems , and optimization for sustainable energy and transportation . He collaborates with researchers on projects like wind farm control, hydrogen aviation networks, and chromatography process optimization. His methodological work on sum-up rounding , trust-region algorithms , and combinatorial integral approximation has been published in journals such as SIAM Journal on Optimization, Mathematical Programming, and IEEE Control Systems Letters. Kirches also serves as area coordinator for Optimization Online and was associate editor for OR Spectrum (2022–2024). Scientific Awards: Klaus-Tschira Prize (2011) Hengstberger Prize (2014) ERC Consolidator Grant (2022) He is an elected member of the COIN-OR initiative and contributes to open-source optimization software. His lab at TU Braunschweig develops algorithms for dynamic systems under uncertainty, with applications in energy management, autonomous traffic, and industrial processes.

• Battery Recycling
• Separation Processes
• Chemical Engineering
• +2 more

Kai M. Schröder is a Researcher at the Institute for Chemical and Thermal Process Engineering , part of the Faculty of Mechanical Engineering at Technical University of Braunschweig. He specializes in battery recycling and separation processes, with a focus on optimizing distillation technologies. PhD: 2024–present – Research assistant at ICTV. Master: 2022–2024 – Biochemical Engineering at TU Braunschweig. Bachelor: 2017–2022 – Biochemical, Chemical, and Pharmaceutical Engineering at TU Braunschweig. His research spans chemical and thermal process engineering , particularly in the BMBF-funded ElRec project for recycling lithium-ion battery components. He has contributed to studies on distillation column performance and supervised lab courses in distillation and advanced fluid separation.

Adnan SalihbegovicView profile

Lecturer

• Automation Engineering
• Instrumentation
• Embedded Systems
• +3 more

Adnan Salihbegovic is affiliated with the Department for Automation and Electronics, where he teaches advanced courses such as Special Measurements Techniques for undergraduate students. His curriculum emphasizes modern instrumentation methodologies, including LabVIEW programming, virtual instrumentation, and data acquisition system integration using DAQ modules and GPIB communication protocols. He oversees laboratory works involving HP and National Instruments equipment for signal analysis, logic analyzers, spectral analyzers, and oscilloscopes. His research interests focus on automation systems, embedded systems design, IoT applications, and control engineering. He has contributed to projects involving web-based SCADA systems, industrial automation workflows, and optimization of order-picking processes in logistics. His work also explores educational tools such as decompilable code editors and tiny OS kernels for teaching purposes. Salihbegovic’s publications span topics like enterprise GIS in power distribution, domain-specific modeling languages for IoT, and vibration control using fuzzy logic. He actively participates in international conferences related to automation and information technologies, demonstrating expertise in both theoretical and applied engineering solutions. His teaching integrates practical lab exercises with theoretical concepts, ensuring students gain hands-on experience with industry-standard tools like LabVIEW and DAQ systems. He also advocates for remote laboratory access and internet-based control systems to enhance educational accessibility and industrial efficiency.

Gürkan SinView profile

Professor

• Process Design and Optimization
• Process Systems Engineering
• Environmental Engineering
• +6 more

Gürkan Sin is a Professor in the Department of Chemical and Biochemical Engineering at the Technical University of Denmark (DTU). He leads research in process systems engineering, focusing on interdisciplinary challenges in chemical, biotechnology, and pharmaceutical industries. His work integrates process modeling, simulation, optimization under uncertainty, and environmental impact analysis. Key affiliations include the KT Consortium and PROSYS (Process and Systems Engineering Centre). Education: PhD from Ghent University (2004), MSc and BSc from Middle East Technical University (METU, Turkey). Prior to DTU, he was an Assistant Professor there from 2008–2010. Research interests span process design for wastewater treatment, biorefineries, and biopharmaceuticals, with emphasis on sustainability and AI-driven solutions. Notable contributions include global sensitivity analysis, Monte Carlo methods, and virtual sensor technologies for large-scale processes. Advises PhD students in areas like machine learning for molecular design, risk assessment in chemical processes, and sustainable energy systems. Projects often involve collaborations with industry partners for practical implementation. His work contributes to UN Sustainable Development Goals, particularly in clean energy and responsible consumption. Labs/Teams: PROSYS Centre and KT Consortium, where interdisciplinary teams tackle complex industrial challenges through advanced modeling and data analytics.

Abhishek SivaramView profile

Researcher

• Chemical Engineering
• Artificial Intelligence
• Machine Learning
• +4 more

Abhishek Sivaram is a Postdoctoral Researcher in the Department of Chemical and Biochemical Engineering at the Technical University of Denmark (DTU), affiliated with the KT Consortium PROSYS - Process and Systems Engineering Centre. His research integrates chemical engineering with artificial intelligence to solve complex process challenges. His primary research spans Chemical Engineering, Machine Learning, and Process Control, with specific expertise in photobioreactor systems, microalgae cultivation, and hybrid modeling approaches. He develops frameworks combining first-principles engineering models with data-driven algorithms for applications ranging from bioreactor optimization to protein precipitation processes. Recent publications reveal a strong emphasis on machine learning integration across chemical engineering domains, particularly using hybrid models to enhance prediction accuracy in multiphase systems and bioprocesses. Key application areas include gas-liquid reactors, single-cell cultivation dynamics, and metabolic flux analysis in cell cultures. Sivaram serves as supervisor for the active PhD project 'Biomolecule Production Mapping via Biosynthesis using Quantum Generative Artificial Intelligence' (2024-2027), collaborating with Prof. S. S. Mansouri and other researchers. This project has attracted significant academic interest with 20 Mendeley readers. He operates within DTU's PROSYS research environment, which specializes in advanced process systems engineering and control strategies for sustainable chemical production.

Teddy GrovesView profile

Researcher

• Metabolic Networks
• Bayesian Regression
• Cell Metabolism
• +12 more

Teddy Groves is a Tenure Track Researcher at the Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark (DTU), focusing on quantitative modeling of cell metabolism and computational methods. His work bridges systems biology, machine learning, and biotechnology. Research Themes : Quantitative cell metabolism, neurovascular coupling, CRISPR-based apoptosis resistance, and data-driven bioreactor optimization. Supervision : Actively supervises PhD students in projects related to human glycolysis, multiscale modeling, and cyanobacteria enzyme allocation. Key Contributions : Developments in Bayesian regression, high-dimensional pathway visualization, and CRISPR knockouts in CHO cells.

Nagarajan PrabakarView profile

Associate Professor

• Distributed Sensor Networks
• Grid Computing
• Computer Networks
• +2 more

Dr. Nagarajan Prabakar is an Associate Professor at Florida International University's Knight Foundation School of Computing and Information Sciences. With research interests spanning distributed sensor networks, grid computing, and spatial data processing, he developed TerraFly software for real-time spatial data access. His work has secured $13.7M in research funding through roles as Principal Investigator and Co-Investigator. Prabakar holds a PhD in Computer Science from the University of Queensland (1985), M.E. in Automation from Indian Institute of Science (1979), and B.E. from Annamalai University (1977). His current research focuses on fault-tolerant distributed computing grids with sensor networks. Professional service includes roles as Undergraduate Committee Chair (2006-2012, 2014-present), Faculty Senator (2011-2013), and University Curriculum Committee Chair (2010-2012). His publications demonstrate expertise in machine learning applications for cybersecurity, environmental monitoring, and quantum computing optimization.

• 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.