Find Academic Supervisors

Mark McQuillingView profile

Associate Professor

• Experimental Fluid Mechanics
• Low Reynolds Number Flows
• Laminar-to-Turbulent Transition
• +4 more

Mark McQuilling is an Associate Professor of Aerospace and Mechanical Engineering at Saint Louis University's School of Science and Engineering. He holds a Ph.D. in Engineering from Wright State University, alongside M.S. and B.S. degrees in Mechanical Engineering from the University of Kentucky. His research focuses on experimental fluid mechanics, low Reynolds number flows, laminar-to-turbulent transition, airfoil design, unsteady aerodynamics (turbomachinery and airdrop systems), bio-fluid flows, and flow control. His work integrates advanced fluid dynamics techniques with practical applications in aerospace and biomedical engineering. Dr. McQuilling oversees the Fluid Systems Laboratory, which includes subsonic and supersonic wind tunnels, a water tunnel, and thermal system facilities. These labs support undergraduate and graduate research, with capabilities such as Laser Doppler Velocimetry, DPIV systems, and strain gauge balances. His thermal research spans micro-scale fluid phenomena to planetary atmospheric modeling, including studies on Uranus/Neptune vortex dynamics and airdrop parachute aerodynamics. Highlighted research areas include low-pressure turbine blade aerodynamics, parachute drag prediction, and bio-fluid studies like pharyngeal airflow analysis in sleep apnea patients. His peer-reviewed publications (over 20 entries) address topics ranging from micro-air vehicle wing design to thermal management in turbine blades. McQuilling is active in professional organizations like AIAA, ASME, and ASEE, and previously worked at the Air Force Research Laboratory. His email is mark.mcquilling@slu.edu.

Karsten UrbanView profile

Professor

• Numerical Mathematics
• Scientific Computing
• Partial Differential Equations
• +9 more

Prof. Karsten Urban is a Full Professor of Numerical Mathematics at the University of Ulm, leading the Institute for Numerical Mathematics. He holds roles such as Dean of Studies in Computational Science and Engineering (CSE) and Deputy Spokesman for the Research Association for Scientific Computing in Baden-Württemberg. He is an active member of prestigious societies including the Deutsche Mathematikervereinigung (DMV) and SIAM. His academic journey includes a PhD from RWTH Aachen (1995), Habilitation (2001), and a full professorship at Ulm since 2005. Research focuses on numerical methods for PDEs, reduced basis techniques, multiscale simulations in fluid mechanics, biomechanics, quantum sciences, and financial mathematics. He has pioneered wavelet-based methods and collaborated with industries on ship propulsion and energy trading models. His work integrates mathematical rigor with real-world applications, emphasizing model reduction and computational efficiency. Editorial Roles: Managing Editor of Advances in Computational Mathematics , Editor of SN Partial Differential Equations and Applications . Awards: Teaching award of Baden-Württemberg (2005), Science-Economy Cooperation Awards (2004, 2008). Administrative Roles: Member of the University Council and ASIIN expert committee. Supervises doctoral students in numerical analysis, quantum simulations, and biomechanics. Active in interdisciplinary projects, including quantum systems (IQST) and fracture healing modeling in collaboration with biomechanics experts. His contributions bridge academia and industry, driving innovation in computational methods.

Dan M. IonelView profile

Professor

• Alternative and Renewable Energy Technologies
• Electric Machines and Power Electronic Drives
• Electromagnetic Devices
• +3 more

Dan M. Ionel, Professor and inaugural L. Stanley Pigman Chair in Power at the University of Kentucky (UK), directs the Power and Energy Institute Kentucky (PEIK). He is affiliated with the SPARK Laboratory in the Department of Electrical and Computer Engineering, focusing on renewable energy, electric machines, and smart grids. Alternative and Renewable Energy Technologies Electric Machines and Power Electronic Drives Electromagnetic Devices Electric Power Systems Energy Storage Smart Grids and Buildings His research integrates machine learning with electromagnetic design for applications like electric vehicles and aircraft propulsion. Recent work includes axial flux permanent magnet machines with Halbach arrays and cryogenic thermal management systems. Scientific awards include IEEE Fellowship. Research is sponsored by NSF, DOE, NASA, and industry partners like ANSYS, EPRI, and Regal Rexnord. Collaborations span national labs (NREL, ORNL), utilities (LG&E, TVA), and aerospace entities.

Fahim KhanView profile

Assistant Professor

• Computer Graphics
• Data Visualization
• Computer Vision
• +5 more

Fahim Khan is an Assistant Professor in the Department of Computer Science and Software Engineering at California Polytechnic State University’s College of Engineering. He specializes in computer graphics, data visualization, computer vision, and machine learning, with a focus on applying these technologies to education, environmental monitoring, and public safety. His research emphasizes making complex data accessible through advanced tools, bridging the gap between raw data and actionable insights. He is deeply committed to inclusive education and fostering interdisciplinary collaboration. His work often integrates citizen science initiatives, empowering communities through mobile applications and machine learning. Notable projects include real-time rip current detection systems and platforms for high school students to engage in research. Khan advocates for equity in technology, designing inclusive learning environments and promoting diversity in STEM. He actively supports the university’s Learn by Doing philosophy, blending practical education with theoretical rigor. Professionally, he contributes to coastal observation networks and autonomous vehicle datasets while maintaining a balance through outdoor activities like exploring Pismo Beach. His research trends reflect a strong focus on mobile computing, environmental applications, and education technology, with recent efforts emphasizing citizen science and data-driven solutions. While no formal grants or advising records are detailed, his projects implicitly involve collaborative efforts. He is affiliated with labs focused on environmental monitoring and mobile technology development, though specific lab names are not mentioned.

• Computational Fluid Dynamics
• Turbomachinery
• Aerodynamics
• +4 more

Dr. Kidambi Sreenivas is an Associate Professor in Mechanical Engineering at the University of Tennessee at Chattanooga (UTC), affiliated with the College of Engineering and Computer Science. He holds a PhD in Mechanical Engineering and specializes in computational fluid dynamics (CFD), with a focus on unstructured multi-physics flow solvers and applications in aerospace, environmental systems, and biomedical engineering. His research bridges academia and industry, collaborating with NASA, the U.S. Navy, Department of Energy, and private companies. Dr. Sreenivas' research interests include rotating machinery simulations, pre-conditioners for non-ideal fluids, and real-world applications such as submarine hydrodynamics, wind farm optimization, aerodynamic efficiency of vehicles, and contaminant dispersal modeling. He has pioneered methods for simulating complex geometries and physics, including high-fidelity simulations of hypersonic vehicles, weapons bay cavities, and shock-wave interactions. Recent work emphasizes advanced CFD methodologies for high-speed flows, thermal effects on turbulence, and aerothermal characteristics of hypersonic test articles. His collaborations have led to practical solutions for drag reduction on Class 8 trucks and improved accuracy in wind turbine modeling. Dr. Sreenivas also contributes to educational initiatives, such as developing PIV systems for undergraduate fluid mechanics labs. His advising and grants reflect partnerships with federal agencies and private sectors, focusing on projects like microplastic sampling devices for stormwater management. These projects highlight his interdisciplinary approach to solving real-world engineering challenges through cutting-edge computational methods.

Enkelejda MihoView profile

Professor

• Digital Life Sciences
• Medical Informatics
• Artificial Intelligence in Healthcare
• +4 more

Prof. Enkelejda Miho is a Professor of Digital Life Sciences at the School of Life Sciences, FHNW, leading the aiHealthLab. Her work bridges computer science/AI with life sciences, focusing on drug discovery, personalized medicine, and immunology. She holds roles as Team Leader at aiHealthLab and Group Leader at the Swiss Bioinformatics Institute. Research Interests : She applies machine learning to analyze immune repertoires, antibody engineering, and autoimmunity diagnostics. Her lab develops computational tools like the RWD-Cockpit for real-world data analysis and synthetic antibody-antigen models (Absolut!) to advance biotherapeutics. Her work on dengue immunity and monoclonal gammopathies highlights translational applications. Key Projects : The aiHealthLab focuses on AI-driven diagnostics and therapeutics. Her contributions include AI frameworks for antibody specificity prediction, age-related immune repertoire changes, and large-scale network analysis of antibody repertoires. Labs/Teams : Leads aiHealthLab and collaborates with the Swiss Bioinformatics Institute, integrating computational and experimental immunology.

Kevin PipeView profile

Professor

• Thermal Sciences
• Energy
• Micro/Nano Engineering
• +3 more

Kevin Pipe is a Professor of Mechanical Engineering, Applied Physics, and Electrical Engineering at the University of Michigan’s College of Engineering, where he also serves as Associate Dean for Undergraduate Education. He holds a Ph.D. (2004) and dual bachelor's/master's degrees (1999) in Electrical Engineering from MIT. His research focuses on microscale heat transfer in electronic/optoelectronic devices, thermoelectric energy conversion, and medical thermal applications. Key projects include molecularly engineered high-conductivity polymers, phase-change materials for thermal management, and cryoanesthesia devices. His work has been featured in Wired and Phys.org , and he received the DARPA Young Faculty Award (2009) and ME Achievement Award (2008). Research collaborations include work with MSE Professor Jinsang Kim’s group on polymer thermal conductivity and computational sprinting projects with computer science partners. His lab explores thermal interfaces in high-power diode lasers, spinal cool-sensing circuits, and ultra-rapid cooling anesthesia systems. Over 60 peer-reviewed articles and patents document his contributions to thermal sciences and materials engineering. Current efforts emphasize translating lab innovations into clinical and industrial applications. Education: Ph.D. in EE, MIT (2004) M.Eng. in EECS, MIT (1999) S.B. in EECS, MIT (1999) Key Awards: Defense Advanced Research Projects Agency Young Faculty Award (2009) ME Achievement Award (2008) Lab Focus Areas: Thermal management of microelectronics Thermoelectric materials Medical thermal devices Grant activities include DARPA-funded projects and industry partnerships. His interdisciplinary approach bridges mechanical, electrical, and biomedical engineering to address thermal challenges in computing and healthcare.

Aurèle MaheoView profile

Research Fellow

• Distributed Systems
• Parallel Computing
• Machine Learning Systems
• +3 more

Aurèle Maheo is a Research Fellow at the Computer Science Department of Telecom SudParis, affiliated with the Parallel and Distributed Systems (PDS) group. His research focuses on distributed systems, parallel computing, and hardware acceleration, with applications in machine learning and high-performance computing. He has contributed to projects such as SpeedyLoader for optimizing machine learning pipelines and FPGA memory bandwidth improvements. His work bridges theoretical distributed systems principles with practical implementation challenges in large-scale environments. Affiliations: PDS Group, Computer Science Department, Telecom SudParis Research interests include distributed consensus algorithms, system scalability, and hardware-software co-design for accelerating computational workflows. Recent work highlights include presentations on machine learning pipelining techniques and FPGA-based memory optimization strategies.

Jiannan WangView profile

Associate Professor

• Database Systems
• Data Management
• Data Cleaning
• +5 more

Jiannan Wang is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). He holds a Ph.D. from Tsinghua University (2013) and a B.Sc. from Harbin Institute of Technology (2008). His research focuses on database systems, data management, and data science, with particular emphasis on data cleaning, crowdsourcing, and big data technologies. He leads the SFU Data Science Research Group, aiming to accelerate data science workflows through innovative tools like DataPrep and ConnectorX. Education: Ph.D. in Computer Science and Technology, Tsinghua University, China (2013) B.Sc. in Computer Science and Technology, Harbin Institute of Technology, China (2008) Research Interests: Dr. Wang's work spans database systems, data cleaning, crowdsourcing, and big data education. He develops open-source tools for data scientists to streamline data preparation and analysis. His lab's mission is to make data science more efficient through technologies like DataPrep and ConnectorX . Awards: IEEE TCDE Rising Star Award (2018) CS-Can|Info-Can Outstanding Early Career Researcher Award (2020) VLDB Best Experiments, Analysis & Benchmark Paper Award (2021) PVLDB Distinguished Review Board Member Award (2020) Advising & Leadership: Director of SFU's Professional Master's Program in Big Data and Visual Computing. Supervised over 20 graduate and undergraduate students, many of whom have gone on to roles at top companies like Google, Amazon, and Huawei. Lab & Teams: Part of the SFU Data Science Research Group and the SFU Big Data Academic Advisory Committee. His lab collaborates with industry partners and contributes to open-source projects in data management and machine learning.

• Software Engineering
• Industrial Automation
• Cloud Computing
• +7 more

David Hästbacka is an Associate Professor (tenure track) at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences at Tampere University. His research focuses on software engineering, industrial automation, and energy systems, emphasizing system architecture, interoperability frameworks, and dependable IoT solutions. He leads a research group exploring edge and cloud computing, semantic integration, and smart energy systems. Education & Professional Background : While specific educational details are not provided, his academic career includes roles such as Postdoctoral Researcher in the SEMIS project (2017-2020) and extensive involvement in EU-funded initiatives like COCOP (EU H2020) and Horizon Europe projects. Research Projects : Active in high-impact projects like Hedge-IoT (Horizon Europe, 2024-2027), TwinfFlow (Business Finland), and TRINEFLEX (Horizon Europe), with a focus on industrial automation, distributed systems, and energy grids. Past projects include FEMMa (Business Finland), DisMa (Academy of Finland), and Arrowhead (ECSEL). Teaching & Supervision : Specializes in Web/Cloud architectures, IoT systems, and dependable automation technologies. Supervises students in topics like edge computing frameworks and MLOps pipelines. Technical Contributions : Develops frameworks for industrial interoperability (e.g., OPC UA PubSub integration), edge-cloud toolchains, and MLOps methodologies. His work addresses challenges in microservices, Kubernetes distributions, and semantic data integration. Labs & Teams : Leads a research group advancing automation technologies through interdisciplinary collaboration, with partnerships in industry and academia to bridge theory and practice in smart systems.

Nicola McConkeyView profile

Lecturer

• Neutrino Physics
• Quantum Technology
• Detector Development
• +2 more

Nicola McConkey is an Ernest Rutherford Fellow and Lecturer in Particle Physics at the School of Physical and Chemical Sciences, Queen Mary University of London. She joined the Particle Physics Research Centre in 2024 and leads experimental work in neutrino interactions and detector development. Her affiliations include the Centre for Fundamental Physics and Centre for Experimental and Applied Physics. McConkey is an active member of international collaborations including SBND, DUNE, and MicroBooNE, where she contributed to the assembly of SBND and pioneered high-statistics measurements of electron-neutrino interactions using liquid argon detectors. Her research focuses on three primary domains: neutrino physics (particularly neutrino-argon scattering cross-sections), quantum technology applications for neutrino mass measurement, and liquid argon time projection chamber (LArTPC) detector development. McConkey's investigations aim to advance fundamental particle physics through precision measurements and technological innovation, with emphasis on improving detection capabilities for next-generation neutrino experiments. Publications predominantly explore neutrino interaction dynamics, cross-section measurements, and detector performance optimizations across MicroBooNE, SBND, and DUNE collaborations. Research trends demonstrate consistent focus on refining LArTPC technologies, developing machine learning applications for particle reconstruction, and probing beyond-Standard-Model physics through neutrino interactions. Scientific Awards: Ernest Rutherford Fellowship (2022) McConkey advises two PhD students (Oscar Chow, Yoshita Dabburi) and leads significant research funding including: STFC Grant: 'Piecing together the neutrino mass puzzle' (£431,666; 2024-2027) STFC Outreach Grant: 'Quantum Technologies for Neutrino Mass' (£99,999; 2024-2025) She coordinates research within the Particle Physics Research Centre laboratory and collaborates extensively within the SBND, DUNE, and MicroBooNE international teams, alongside leading the Quantum Technologies for Neutrino Mass collaboration developing novel measurement techniques.

• Superconductivity
• High-Field Magnets
• Particle Accelerators
• +3 more

Tiina Salmi is an Academy Research Fellow specializing in superconductivity and high-field magnet systems. She holds a Doctor of Science (Technology) in Electrical Engineering (2015) and a Master of Science in the same field (2009). Her research focuses on quench protection systems, superconducting magnet design for particle accelerators (e.g., Future Circular Collider, Muon Collider, and LHC upgrades), and material behavior under extreme conditions. Affiliations: Academy of Finland, collaborating with institutions like CERN and LARP. Key Projects: FCC dipole development, Muon Collider storage ring magnets, and HTS tape mechanical analysis. Editorial Roles: Editorial board member for IEEE Transactions on Applied Superconductivity (2020–present). Research Interests Her work addresses critical challenges in superconducting magnet technology, including quench dynamics, thermal management, and high-field stability. She employs computational models (e.g., FEM simulations) and AI-driven approaches to optimize magnet performance and safety. Recent focus areas include: Superconducting materials (Nb 3 Sn, ReBCO, HTS) under mechanical/thermal stress CLIQ protection systems for FCC-hh dipoles Surrogate models for predicting heater delays Mechanical behavior of 2G HTS tapes Grants & Activities Active in international collaborations like the Muon Collider Project and the LARP (LHC Accelerator Research Program). Conducted over 80 peer-reviewed publications (2011–2025) and presented at conferences since 2013. Her work aligns with SDGs 9 (Industry, Innovation) and 12 (Sustainable Consumption). Future Work Ongoing projects include optimizing superconducting magnets for next-gen colliders and advancing AI applications in magnet design. Exploring sustainable materials and cost-effective high-field magnet solutions for future accelerators.

• Computer Security
• Cryptography
• Distributed Systems
• +3 more

Nickolai Zeldovich is the Joan and Irwin M. Jacobs Professor of Electrical Engineering and Computer Science at MIT, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from Stanford University in 2008 and focuses on building practical secure systems, including encrypted databases, undefined behavior detection tools, formally verified file systems, and cryptocurrency protocols. His work spans both theoretical and applied aspects of computer security and distributed systems. Research interests include: Secure system design Distributed consensus mechanisms Formal verification of software/hardware Cryptographic protocols Privacy-preserving web technologies Publications & Verification Tools Recent work focuses on modular verification of complex systems, including: Shipwright (2025): Byzantine-fault-tolerant distributed system verification PoWER (2025): Crash consistency verification framework K2 Architecture (2023): Trustworthy hardware security modules Grove (2023): Separation-logic-based verification library Tiptoe (2023): Private web search protocols Major Awards Best paper award, ACM SOSP (2011, 2015, 2017) Sloan Research Fellowship (2010) NSF CAREER award (2011) MIT Jamieson Award for Teaching (2024) Active in multiple startup ventures including Algorand (cryptocurrency), MokaFive (virtualization), and PreVeil (end-to-end encryption).

• Machine Learning
• Database Systems
• Artificial Intelligence
• +3 more

Christopher Re is a Professor in the Department of Computer Science at Stanford University, affiliated with the Stanford AI Lab, Machine Learning Group, and Center for Research on Foundation Models. His research focuses on the intersection of machine learning, database systems, and scientific computing, with applications in humanitarian efforts, scientific discovery (e.g., extrasolar neutrinos, DNA foundation model Evo), and industry partnerships with companies like Apple and Google. He has been recognized with prestigious awards, including the MacArthur Foundation Fellowship and multiple test-of-time awards. His work emphasizes advancing thermal materials, phase-change memory, and ultrafast electron microscopy technologies. Re's research contributions span database theory, systems, and machine learning, with best papers at PODS 2012, SIGMOD 2014, and ICML 2016. His lab’s innovations have been incorporated into products globally, and he actively invests in technology startups. Key projects include developing thermal interface materials for 3D integrated circuits and exploring energy-efficient neuro-inspired memory systems. His awards reflect sustained excellence: NeurIPS 2020 and PODS 2022 test-of-time awards, along with recent accolades for student-led initiatives at MIDL 2022 and ICLR22. Re’s interdisciplinary approach bridges academia and industry, driving both scientific and humanitarian impact.

• Offshore Renewable Energy
• Floating Offshore Wind Turbines
• Multi-purpose Offshore Structures
• +7 more

Maurizio Collu is a Professor in Offshore Renewable Energy Engineering at the Department of Naval Architecture, Ocean and Marine Engineering, Faculty of Engineering, University of Strathclyde, where he has been a faculty member since August 2018. He is actively leading high-impact research in offshore renewable energy systems and holds leadership roles in international committees and editorial boards. His research focuses on the applied mechanics and multidisciplinary modeling of dynamics for offshore renewable energy systems. Key areas include the analysis and design of floating offshore wind turbines and multi-purpose offshore platforms. His work integrates hydrodynamics, structural mechanics, and control systems to develop advanced aero-hydro-servo-elastic coupled models. He is particularly known for pioneering research in floating wind-hydrogen integration and AI-driven offshore maintenance. The recent publications highlight a strong trend toward integrated offshore energy systems, with increasing focus on hydrogen production, fatigue resilience under multiple sea states, and optimization of spar and semisubmersible floating platforms. His work leverages high-performance computing and data-driven methods to improve design and operational feasibility. Calder Prize (2011) Maurizio Collu has directly managed approximately £4.5 million in public research funding across projects totaling £28.5 million, funded by EPSRC, NERC, Innovate UK, ORE Catapult, EU H2020, and others. He is the Principal Investigator or Co-Investigator in major projects such as Ocean RE-Fuel (floating wind-to-hydrogen), The Blue Growth Farm (multi-functional offshore platforms), INNO-MPP (UK-China collaboration), and HOME-Offshore (AI for wind farm maintenance). He actively supervises PhD and postgraduate researchers and collaborates with industry partners ranging from startups to multinational energy firms. He is involved in key research teams and labs focused on offshore renewable energy, including the research group at the University of Strathclyde's Department of Naval Architecture, Ocean and Marine Engineering. He also contributes to international consortia such as the ITTC Ocean Renewable Energy Committee and serves as an advisor to SINTEF Ocean. His work is part of a broader network advancing sustainable offshore energy solutions aligned with UN Sustainable Development Goals.