Find Academic Supervisors

• Polymer Dynamics
• Liquid-Liquid Phase Separation
• Electrochromism
• +4 more

Yuecheng Zhou is an Assistant Professor at the University of Illinois, affiliated with the departments of Materials Science and Engineering, Bioengineering, the Materials Research Lab, and the Beckman Institute for Advanced Science and Technology. His research focuses on polymer dynamics, liquid-liquid phase separation, and electrochromic materials for biomedical applications. Zhou’s work bridges fundamental polymer science with practical applications in biomedicine and material design. His research interests include studying single polymer dynamics under various flow conditions, developing optical recording techniques for bioelectric potentials using electrochromic materials, and investigating the rheological behavior of complex polymer solutions. He has contributed significantly to understanding the dynamics of ring-linear polymer blends and the role of molecular architecture in non-equilibrium systems. Zhou’s recent publications highlight advancements in label-free optical detection of cellular signals, liquid-liquid phase separation in synthetic biosystems, and theoretical insights into viscoelastic hysteresis using fluctuation theorems. These studies underscore his interdisciplinary approach, combining experimental and computational methods to address challenges in materials science and biophysics. His work is supported by affiliations with leading research institutes at the University of Illinois, enabling collaborative projects across engineering, physics, and biology. Zhou’s research has implications for developing novel materials for biomedical diagnostics, energy storage, and advanced sensor technologies.

André LongtinView profile

Professor

• Nonlinear Dynamics
• Stochastic Dynamics
• Theoretical Biophysics
• +6 more

André Longtin is a Full Professor in the Department of Physics at the University of Ottawa, affiliated with the Faculty of Science. He co-directs the Centre for Neural Dynamics & Artificial Intelligence and holds cross-appointments in Cellular and Molecular Medicine, Mathematics and Statistics. His research focuses on nonlinear dynamics, stochastic systems, and computational neuroscience, with applications to sensory processing, neural coding, and thermodynamic principles in biological systems. Education: B.Sc. (Honours Physics, Université de Montréal), M.Sc. (Physics, Université de Montréal), Ph.D. (Physics, McGill University, 1989). Postdoctoral training at Los Alamos National Laboratory. Research Interests: Theoretical biophysics, neural modeling, entropy production in biological systems, delayed dynamical systems, and interdisciplinary applications of nonlinear science. His group explores computations in hippocampus and zebrafish pallium, epilepsy mechanisms, neural coding in electrosensory systems, and autonomous stochastic rhythms. Recent Work: Articles highlight entropy dynamics in humans, myelin plasticity effects on neural networks, and reservoir computing with delayed loops. Awards include APS Fellowship (2003) and the NSERC Brockhouse Prize (2017). Editorial Roles: Biological Cybernetics, Frontiers in Computational Neuroscience, and others. Collaborations span systems neuroscience, clinical specialties, and engineering.

Ingrid MoermanView profile

Professor

• Wireless Networking
• Cognitive Radio Networks
• Internet of Things
• +4 more

Ingrid Moerman is a part-time Professor at Ghent University and a staff member at the Internet Technology and Data Science Lab (IDLab), a core research group of imec embedded within Ghent University and the University of Antwerp. She coordinates mobile and wireless networking research and leads a team of over 30 researchers at Ghent University, with extensive involvement in European and national funding initiatives. She received her Electrical Engineering degree (1987) and Ph.D. (1992) from Ghent University. Her research spans collaborative networks, cognitive radio, software-defined radio, IoT, LPWAN, and high-density wireless access, emphasizing experimentally-supported development of next-generation wireless systems with practical implementations in spectrum management and real-time control. Recent publications (2024-2025) reveal a strong pivot toward AI-integrated wireless networking, featuring OFDMA scheduling innovations, Wi-Fi 6/7 interference mitigation, and time-sensitive networking for industrial applications. Key trends include 5G/6G convergence, vehicular communication enhancements, and digital twin frameworks for network observability, reflecting her focus on mission-critical industrial use cases. Her accolades include: 9 Best Paper Awards 2 FWO Prizes (Research Foundation - Flanders) IMEC Prize of Excellence 2001 MSc Thesis Award (as promoter) Best Demo/Exhibit Award at ICT 2013 DARPA Spectrum Collaboration Challenge Prize ($750,000) She has coordinated major EU projects (FP7/H2020: CREW, WiSHFUL, eWINE, ORCA) with industry partners, securing substantial funding for experimental wireless research. Her grant portfolio emphasizes collaborative innovation in spectrum sharing and neutral-host architectures for multi-operator environments. At IDLab, she directs advanced wireless testbeds supporting real-world validation of technologies like openwifi and White Rabbit, with active experimentation in time-sensitive networking and spectrum collaboration for industrial IoT deployments.

Jo KennedyView profile

Associate Professor

• Financial Engineering
• Quantitative Finance
• Mathematical Finance
• +3 more

Dr. Jo Kennedy is an Associate Professor in the Department of Statistics at the University of Warwick, where she has been since 1998. Previously, she held positions at the University of Oxford and the University of Bristol. Her research focuses on financial derivatives, particularly interest rate derivatives and modeling requirements for market practitioners. She co-authored the influential book Financial Derivatives in Theory and Practice (2nd ed., 2004) with Phil Hunt. Kennedy holds a PhD in probability theory from the University of Cambridge, following her undergraduate and MSc degrees from the University of Sydney. Her work emphasizes advanced stochastic models, including Markov-functional models, stochastic volatility, and the SABR framework. Recent publications explore cross-currency models, non-Gaussian drivers, and the implications of model choice for hedging strategies. Her research bridges theoretical finance with practical applications in derivatives pricing and risk management. Despite her prolific output, no scientific awards are explicitly mentioned in the provided text. She collaborates actively with colleagues like J. Gogala, D. Pham, and L. Kaisajuntti, contributing to computational finance and applied mathematical finance journals. Her office is located in Room 3.06 of the MSB building at the University of Warwick.

Maria Grazia De AngelisView profile

Professor

• Polymer Science
• Gas Separation Membranes
• Medical Engineering
• +3 more

Professor Maria Grazia De Angelis holds a Personal Chair in Thermodynamics of Materials and Processes at the University of Edinburgh's School of Engineering. Her research focuses on advanced materials for gas separation membranes, medical engineering innovations like wearable artificial kidneys, and environmental applications such as CO2 capture. Key projects include the development of semicrystalline polymers for hydrogen storage and nanoporous materials for dialysate regeneration. She leads interdisciplinary research teams addressing challenges in sustainable materials and biomedical technologies. Her work combines computational modeling, molecular simulations, and machine learning to design next-generation materials. Notable contributions include breakthroughs in superhydrophobic materials and CO2 capture membranes. Funded projects span EPSRC, Kidney Research UK, and industry partners like Danisco. Collaborations involve global institutions, advancing both fundamental and applied research in materials science.

Buz BarstowView profile

Assistant Professor

• Synthetic Biology
• Sustainable Energy
• Bioengineering
• +2 more

Buz Barstow is an Assistant Professor in the Department of Biological and Environmental Engineering at Cornell University's College of Agriculture and Life Sciences. His work focuses on developing sustainable solutions for energy and environmental challenges through synthetic biology approaches. Professor Barstow leads research in microbial engineering for resource recovery, with specific emphasis on rare earth element bioleaching using engineered organisms like Gluconobacter oxydans . His team investigates bio-accelerated weathering processes, microbial transformation techniques, and sustainable methods for critical metal recovery from waste streams. Recent publications demonstrate consistent focus on: Enhancing bioleaching efficiency through genomic engineering Developing circular economy approaches for industrial wastewater Advancing fundamental understanding of microbe-mineral interactions Creating low-carbon biotechnology platforms Laboratory facilities include specialized equipment for microbial cultivation, molecular biology, and materials characterization that support interdisciplinary investigations at the biology-engineering interface.

SHEN LeiView profile

Researcher

• Multiscale Modeling and Simulation
• Materials Informatics
• Computational Physics
• +3 more

SHEN Lei is a researcher at the National University of Singapore (NUS), affiliated with the Department of Physics. With a PhD in Physics from NUS, he specializes in Multiscale Modeling and Simulation and Materials Informatics , leveraging machine learning and computational methods for advanced materials discovery. Research Focus: Density functional theory, molecular dynamics, finite element analysis, and data-driven design of materials. Teaching: Modules include Mechanics and Waves (PC1433), Applied Quantum Mechanics (PC2130B), and Mechanical Properties of Materials (ESP2109). His work spans spintronics, ferroelectricity, and energy storage materials, with recent publications on interatomic potentials, sliding heterostructures, and battery anodes. He has received the Teaching Commendation Award and declined the Lee Kuan Yew Postdoctoral Fellowship . Notable Trends: Recent articles emphasize machine learning in materials science, van der Waals heterostructures, quantum transport, and medical image analysis. Subfields include Rashba spin-orbit coupling, piezoelectric tensor modeling, and defect-informed neural networks. Scientific Awards: Teaching Commendation Award (AY15/16; AY16/17) Lee Kuan Yew Postdoctoral Fellowship (2014) (declined)

Huaiying ZhangView profile

Assistant Professor

• Biomolecular Condensates
• Cancer Biology
• Cellular Functions
• +3 more

Huaiying Zhang is an Assistant Professor in the Department of Biological Sciences at Carnegie Mellon University, part of the Mellon College of Science. His research focuses on the role of biomolecular condensates in cellular functions and cancer progression, particularly investigating phase transitions in telomere maintenance and cancer cell immortality. He holds a Ph.D. from McGill University and completed postdoctoral research at Dartmouth College, Princeton University, and the University of Pennsylvania. Research interests include engineering synthetic organelles, developing optogenetic tools to manipulate phase separation in live cells, and targeting phase transitions for cancer therapy. His work bridges biophysics, cell biology, and synthetic biology to address fundamental questions in nuclear organization and disease mechanisms. Education: Ph.D., McGill University Postdoctoral Fellowships: Dartmouth College, Princeton University, University of Pennsylvania Publications highlight advances in understanding telomere clustering in cancer cells, nuclear body formation, and applications of phase separation in therapeutic strategies. Collaborative projects emphasize interdisciplinary approaches, combining experimental and theoretical methods. Lab activities focus on biomolecular condensates' material properties, their roles in genomic processes, and translational applications in cancer treatment. The lab actively seeks students and researchers interested in cellular biophysics and disease biology.

• Algorithm Design
• Discrete Optimization
• Approximation Algorithms
• +2 more

Nikhil Bansal is Patrick C. Fischer Professor of Theoretical Computer Science at the University of Michigan. His research focuses on algorithms for discrete optimization problems, discrepancy theory, and scheduling. Research Contributions: Developed foundational techniques in randomized rounding and algorithmic discrepancy minimization. Current investigations include online algorithms, approximation methods for scheduling, and quantum-classical complexity separations. Teaching: Offers courses on randomized algorithms, linear algebra, and approximation algorithms. Created instructional materials on discrepancy theory and semidefinite programming. Professional Service: Serves on editorial boards for Journal of the ACM and Theory of Computing. Organized international workshops on discrepancy theory and optimization.

Sai GuView profile

Professor

• Bioenergy
• Carbon Capture
• Catalytic Processes
• +3 more

Professor Sai Gu is a distinguished academic and leader in Chemical Engineering, currently serving as Deputy Pro-Vice-Chancellor (East and South East Asia) at the University of Warwick. He holds a Professorship in Chemical Engineering and has held senior roles at institutions including the University of Surrey, Cranfield University, and Southampton. His expertise spans bioenergy, carbon capture, materials science, and digital transformation in industry. Prof Gu earned his PhD in Material Modelling from the University of Nottingham and has led over £100 million in EPSRC/EU-funded projects, pioneering innovations like Industry 6th Sense technologies and catalytic biomass conversion. Research interests include biomass fast pyrolysis, CO2 capture via novel solvents, thermal spray coatings, and AI-driven manufacturing. He established the Centre for Bioenergy and Resource Management and leads the EPSRC-funded DigitalMetal CDT to advance metals industry digitization. Prof Gu has published 200+ papers (20,000+ citations) and is a Fellow of the Royal Academy of Engineering. His work bridges academia and industry, fostering global collaborations in clean energy and sustainable technologies. Education: PhD in Material Modelling (University of Nottingham), Postdoc at University of Cambridge Awards: FREng (Royal Academy of Engineering) Key Projects: EPSRC CDTs in NetZero and Digital Metals, 'Stepping towards Industrial 6th Sense' Labs: Centre for Connected Plants of the Future (UoS), Centre for Bioenergy (Cranfield)

Raja GhoshView profile

Professor

• Bioengineering
• Membrane Technology
• Biopharmaceutical Processing
• +3 more

Raja Ghosh is a Professor in the Department of Chemical Engineering at McMaster University's Faculty of Engineering. His research focuses on bioengineering and polymer materials, with specialization in membrane technology for biopharmaceutical applications. He is actively involved in the Health & Bio-innovation research cluster and teaches courses such as CHEM ENG 3BM3: Bioseparations Engineering and CHEM ENG 782: Biopharmaceuticals . Research Interests include membrane chromatography device design, bioseparations process development, membrane bioreactors for enzymatic and protein applications, and therapeutic protein stabilization. His work addresses scalable purification methods for monoclonal antibodies, PEGylated proteins, and viral vectors. Recent Publications highlight innovations in cuboid chromatography design, pH-modulated ion-exchange systems, and SARS-CoV-2 spike protein purification. He employs computational fluid dynamics (CFD) simulations to optimize device performance and reduce pressure drops in bioseparation processes. Contact Information : Email: rghosh@mcmaster.ca Office: JHE A408 Phone: 905-525-9140 ext. 27415

Wonbong ChoiView profile

Professor

• Materials Science
• Energy Storage
• Nanomaterials
• +3 more

Dr. Wonbong Choi is a University Distinguished Research Professor at the University of North Texas with joint appointments in the Department of Materials Science and Engineering and Mechanical Engineering. His research focuses on nanomaterials, energy storage systems (particularly lithium-sulfur and zinc-ion batteries), additive manufacturing of composites, and neuromorphic computing devices. He was elected Fellow of the National Academy of Inventors in 2024 for his contributions to materials innovation. Research interests span materials synthesis, electrochemical characterization, and device integration of 2D materials like MXenes and transition metal dichalcogenides. Key areas include: Nanomaterial design for batteries and supercapacitors 3D-printed sensors and structural composites Defect engineering for neuromorphic computing His recent publications highlight consistent themes in MXene synthesis optimization, advanced battery architectures, and multifunctional composites. Article trends show increasing focus on in situ characterization, scalable manufacturing of nanomaterials, and AI-driven materials design. Awards: Fellow, National Academy of Inventors (2024) Collaborates with research groups at Oak Ridge National Laboratory and leads projects on lightweight composites and energy storage systems.

Jennifer TangView profile

Research Fellow

• information theory
• prediction and estimation
• quantization and data compression
• +4 more

Jennifer Tang is a Postdoctoral Associate at the Massachusetts Institute of Technology (MIT), holding dual appointments in the Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS). She conducts her research under Professor Ali Jadbabaie, focusing on interdisciplinary problems at the intersection of information theory, network science, and social dynamics. Her position is temporary as she actively seeks a permanent academic role through the 2025 job market. Her academic credentials include: Ph.D. in Electrical Engineering and Computer Science from MIT, advised by Professor Yury Polyanskiy Bachelor of Science in Engineering (B.S.E.) in Electrical Engineering from Princeton University, with independent work supervised by Paul Cuff Dr. Tang's research program centers on theoretical and applied aspects of information theory, including channel capacity, quantization, and data compression. She investigates prediction and estimation in high-dimensional settings, data analytics for complex systems, and mathematical modeling of social dynamics and inference in multi-agent networks. Her work employs tools from statistics, optimization, and network theory to address challenges in communication, decision-making, and societal systems, with particular emphasis on opinion dynamics under social pressure and efficient representation of probability distributions. Analysis of her publication record reveals consistent contributions to information-theoretic limits, social network modeling, and compression techniques. Her works frequently appear in top venues like IEEE Transactions on Information Theory and major conferences (ISIT, CDC, ACC), demonstrating expertise in bridging theoretical foundations with real-world applications in networked systems and societal challenges. Her scientific achievements have been recognized with: Best Student Paper Award at IEEE International Symposium on Information Theory (ISIT) 2022 Best Student Paper Award at IEEE Machine Learning for Signal Processing (MLSP) 2022 Student Competition Winner at the Shannon Centennial Celebration Dr. Tang maintains an active teaching portfolio, having served as instructor for MIT 1.022: Introduction to Network Models (Spring 2025) and teaching assistant for multiple core courses including 6.008 (Introduction to Inference), 6.041/6.431 (Probabilistic Systems Analysis), 6.437 (Inference and Information), and 6.439 (Statistics, Computation and Applications). She also contributed to the MIT Women's Technology Program as a Mathematics Instructor during summer 2017. Her research is embedded within MIT's Laboratory for Information and Decision Systems (LIDS) and Institute for Data, Systems, and Society (IDSS), two premier interdisciplinary laboratories fostering collaboration on data-driven decision-making, societal challenges, and foundational theory in information and systems.

• Geometallurgy
• Resource Technology
• Mineral Exploration
• +2 more

Jens Gutzmer is Professor and founding Director of the Helmholtz Institute Freiberg for Resource Technology (HIF) at Helmholtz-Zentrum Dresden-Rossendorf. His research focuses on developing sustainable technologies for mineral resource extraction and recycling, with expertise in geometallurgy, mineral characterization, and hydrothermal systems. He holds a visiting professorship at the University of Johannesburg and leads international collaborations in resource technology. Education includes a Diplom in Mineralogy (Technical University of Clausthal-Zellerfeld, 1993) and PhD in Geology (Rand Afrikaans University, 1996). His research investigates mineral deposit formation, resource recovery from secondary sources, and sustainable materials processing. Recent work emphasizes circular economy approaches for critical raw materials. Research encompasses geometallurgical process optimization, 3D mineral quantification, and novel extraction methods. Publications demonstrate consistent focus on resource efficiency, with recent articles exploring electrolyzer recycling, tailings reprocessing, and sustainable electronics design. Awards include: President Award of NRF South Africa (1999) Waldemar E. Lindgren Award (2002) TWAS Award (2007) Mercator Professorship (2003-2004) Directs HIF's research infrastructure including FlexiPlant for recycling 4.0 technologies and metallurgy pilot plants. Leads European Rare Earths Competency Network initiatives and advises Rock Tech Lithium Inc.

• Mathematical Physics
• Partial Differential Equations
• Probability
• +3 more

Felipe Hernández is an Assistant Professor in the Department of Mathematics at Pennsylvania State University. He is affiliated with the Department of Mathematics, located in McAllister Building, and can be contacted at felipeh@psu.edu. His research interests span Mathematical Physics, Partial Differential Equations, and Probability, with a focus on quantum systems, stochastic processes, and theoretical analysis of complex systems. His work explores topics such as quantum diffusion in disordered systems, decoherence dynamics, and the interplay between classical and quantum mechanics. He also investigates computational methods like neural networks to enhance accuracy in quantum chemistry calculations and explores foundational questions in quantum computing, such as BosonSampling complexity. Recent contributions include studies on Hodge systems, divergence-free vector fields, and endpoint estimates in functional analysis. While no specific awards or grants are listed, his research demonstrates a commitment to advancing mathematical physics and computational methods through rigorous theoretical analysis and interdisciplinary approaches.