Find Academic Supervisors

Ulrich LächeltView profile

Assistant Professor

• Nanoparticle Technology
• Pharmaceutical Sciences
• Material Science
• +3 more

Ulrich Lächelt is an Assistant Professor in the Department of Pharmaceutical Sciences at the Faculty of Life Sciences. His research focuses on nanoparticle technology, drug delivery systems, and gene therapy, with a particular emphasis on CRISPR/Cas9 genome editing and RNA-based therapeutics. He leads a project on nanoformulations of prime editing ribonucleoproteins (2025–2029), aiming to advance precision medicine. His work contributes to UN Sustainable Development Goals, particularly in health and innovation. Research Interests: Nanoparticle design and material science CRISPR/Cas9 delivery systems siRNA and mRNA therapeutic formulations Cancer-targeted drug delivery Biomedical engineering applications Publications (2025–2023): Highlighted studies include dual pH-responsive CRISPR delivery systems and accelerated endosomal escape mechanisms. His work spans 40+ peer-reviewed articles, with recent trends focusing on xenopeptide carriers and tumor-targeted therapies. Grants & Projects: Current research funding includes a nanoformulations project (2025–2029). He actively collaborates on international initiatives, with recent presentations at global conferences on CRISPR delivery and gene editing strategies. Labs/Teams: Involved in interdisciplinary teams developing novel drug delivery platforms and screening tools for prime editor RNPs.

Riyadh BaghdadiView profile

Assistant Professor

• Compilers
• Programming Languages
• Deep Learning
• +9 more

Riyadh Baghdadi is an Assistant Professor of Computer Science at New York University Abu Dhabi and a Global Network Assistant Professor at the Tandon School of Engineering, NYU. He is also a Research Affiliate at MIT, where he previously completed a postdoctoral fellowship. His academic journey includes a PhD and Master’s from Sorbonne University (INRIA/UPMC) and an engineering degree from Ecole Supérieure d’Informatique in Algiers. Assistant Professor, NYU Abu Dhabi Global Network Assistant Professor, Tandon School of Engineering, NYU Research Affiliate, MIT His research lies at the intersection of compilers, programming languages, and applied machine learning, with a focus on developing advanced compiler techniques for deep learning, high-performance computing, and data-parallel algorithms. He is the lead developer of the Tiramisu compiler , a polyhedral compiler designed to optimize dense and sparse deep learning workloads across diverse architectures including CPUs, GPUs, and FPGAs. Riyadh’s recent publications demonstrate a strong trend toward integrating machine learning into compiler optimization—particularly in cost modeling, loop scheduling, and automatic code generation. His work addresses critical challenges in optimizing sparse neural networks and enabling efficient execution on resource-constrained platforms like smartphones and autonomous vehicles. Outstanding Paper Award, MLSys 2021 He has mentored 18 students and taught core courses such as Computer Systems Organization and Machine Learning at NYUAD. His service to the academic community includes program committee roles at MLSys, IPDPS, ECOOP, and PACT, as well as organizing workshops on polyhedral compilation and machine learning for hardware-software co-design. Riyadh actively contributes to open-source projects and collaborates with industry leaders including Google, Facebook, NVIDIA, and Intel. He leads the development of Tiramisu and collaborates on DSLs like GraphIt and Halide, focusing on performance portability and automation in compiler design.

Alina EneView profile

Associate Professor

• Theoretical Computer Science
• Algorithms
• Optimization
• +3 more

Alina Ene is an Associate Professor in the Department of Computer Science at Boston University. Her research focuses on theoretical computer science, algorithms, optimization, and theoretical machine learning, with recent work emphasizing submodular optimization and convex/non-convex optimization techniques. Key research areas include Submodular optimization Convex and non-convex optimization Stochastic gradient methods Streaming and parallel algorithms Her recent publications analyze high-probability convergence in optimization, adaptive gradient methods, and submodular function applications in machine learning and network design. Current advisees include PhD students Fabian Spaeh and Ta Duy Nguyen. She has previously mentored students and postdocs such as Andrew Suh, Adrian Vladu, and Rafael da Ponte Barbosa. Alina has taught courses like CS 237 (Probability in Computing), CS 531 (Advanced Optimization Algorithms), and CS 591 E2 (Convex Optimization Algorithms) at Boston University, and earlier courses at the University of Warwick.

Jacob C DunnView profile

Associate Professor

• Animal and environmental biology
• Bioacoustics
• Vocal production
• +7 more

Dr Jacob C Dunn is an Associate Professor of Evolutionary Biology at Anglia Ruskin University's School of Life Sciences. Formerly a Senior Lecturer at Anglia Ruskin (2016–2020) and Lecturer at the University of Cambridge's Division of Biological Anthropology (2012–2016), he leads the Primate Evolution and Ecology Research Group and directs the Behavioural Ecology Research Group . His research focuses on primate vocal communication, language evolution, and conservation applications of bioacoustics, with international collaborations including the Fitch Lab at the University of Vienna. Research Interests: Bioacoustic analysis of primate vocal anatomy Macroevolutionary hypothesis testing Comparative studies of fear evolution Eco-acoustic approaches to animal welfare Phylogenetic comparative methods Notable Grants: £15,000 from Accelerate-C2D3 (2022–2023) £65,000 QR Funding (2021–2022) £340,000 Hydrophis Gas grant (2019–2022) £66,000 Anglia Ruskin University grant (2019–2020) Teaching: BSc (Hons) Zoology, Animal Behaviour, and Ecology & Conservation.

Ellen KuhlView profile

Professor

• Living Matter Physics
• Computational Biomechanics
• Soft Matter Modeling
• +5 more

Ellen Kuhl serves as the Catherine Holman Johnson Director of Stanford Bio-X and the Walter B. Reinhold Professor in the School of Engineering at Stanford University. She holds dual appointments as Professor of Mechanical Engineering and, by courtesy, Bioengineering, leading interdisciplinary research at the convergence of physics, computation, and biology. Her academic credentials include: Habil., TU Kaiserslautern (2004) Ph.D., University of Stuttgart (2000) M.S., Leibniz University of Hanover (1995) B.S., Leibniz University of Hanover (1993) Kuhl pioneers Living Matter Physics , developing computational frameworks that integrate physics-based modeling with machine learning to simulate biological systems across scales. Her work spans cardiovascular dynamics (including the 400-member global Living Heart Project), neurodegenerative disease progression (Alzheimer's tau pathology), and sustainable food systems (mechanics of plant/fungi-based meats). Recent innovations focus on automated model discovery using constitutive neural networks to democratize simulation tools for soft matter systems, with applications in precision medicine and climate-resilient food innovation. Her lab actively bridges engineering fundamentals with urgent societal challenges in healthcare and planetary health. Her publication trajectory reveals accelerating integration of AI with biomechanics, particularly in automated constitutive modeling for diverse tissues and food materials. Key trends include uncertainty quantification in neural networks, physics-informed machine learning for digital twins, and democratization of simulation tools for non-experts – reflecting her commitment to accessible computational science. Major recognitions include: National Science Foundation Career Award (2010) Humboldt Research Award (2016) ASME Ted Belytschko Applied Mechanics Award (2021) ERC Advanced Grant (2024) Fellowships in ASME and AIMBE As Bio-X Director, Kuhl orchestrates major interdisciplinary initiatives connecting engineering with life sciences, securing substantial funding including the 2024 ERC Advanced Grant. Her leadership extends to the US National Committee on Biomechanics and World Council of Biomechanics, while her Living Heart Project demonstrates exceptional translational impact through industry/medical partnerships across 24 countries. The Living Matter Lab operates as a nexus for high-impact research, developing computational tools that transform cardiovascular medicine, decode neurodegenerative mechanisms, and engineer sustainable food alternatives. Current projects leverage AI to accelerate plant-based meat development, model elephant-trunk-inspired soft robotics, and personalize cardiac simulations – all unified by her vision of physics-driven machine learning for global challenges.

• Fluid Mechanics
• Aeroacoustics
• Computational Fluid Dynamics
• +3 more

Antonio Alguacil Cabrerizo is an Assistant Professor (starting 2025) at Université de Sherbrooke, where he currently serves as a Postdoctoral Fellow (2023-2025). His academic trajectory includes dual doctoral degrees in Mechanical Engineering from Université de Sherbrooke and École Nationale Supérieure d'Aéronautique et de l'Espace, complemented by aerospace engineering degrees from ENSEEIHT and Universidad Politécnica de Madrid. His research integrates computational fluid dynamics with machine learning, focusing on: Aeroacoustic prediction and noise source identification Deep learning surrogates for fluid and acoustic systems Turbomachinery and airfoil aerodynamics Data-driven modeling of spatiotemporal physical systems This work advances computational efficiency in simulating complex wave propagation, turbulence effects, and fluid-structure interactions. Publication analysis reveals consistent focus on developing neural network-based computational methods for aeroacoustics and fluid dynamics. His 15 most recent works demonstrate progressive refinement in applying convolutional architectures to predict acoustic scattering, refraction phenomena, and turbomachinery noise with increasing physical accuracy and computational efficiency. Awards and recognition include: Top 5 in AIAA Best Student Paper in Aeroacoustics (2024) Graduate Scholarship Award from CFD Society of Canada (2022) Eureka Scholarship from Université de Sherbrooke (2021) Best Poster Prize at CRASH Day (2021) He secured a $70,000 CAD startup grant (2025-2028) from Université de Sherbrooke for establishing his research program. No student advising relationships or laboratory affiliations are currently documented.

• Atomic Force Microscopy
• Nanotechnology
• Mechatronics
• +4 more

Amir Farokh Payam is a Senior Lecturer in Electronics and Software at the School of Engineering, Ulster University, UK since 2019. He holds a PhD in Electronics Engineering-Nanotechnology and has previously worked at Instituto de Ciencia de Materiales de Madrid (2012–2015), Durham University (2016–2018), and University of Bristol (2018–2019). Education: B.Sc. and M.Sc. in Electrical and Electronics Engineering, PhD in Electronics Engineering-Nanotechnology His research focuses on dynamic Atomic Force Microscopy (AFM) , NEMS/MEMS , Surface Science , and Applied Nonlinear Control . Recent work explores nonlinear harmonics in AFM, solid-liquid interfacial dynamics, and single-cell biomechanical profiling. He leads projects on quantum sensor fabrication and cancer cell viscoelasticity analysis. Key trends in his 15 most recent publications (2025–2023) include advancements in nanoscale imaging , multifrequency AFM , viscoelastic material analysis , and biomedical applications such as viral protein sensing and corneal cell profiling. Collaborative projects span institutions in Ireland, Spain, and the UK. Scientific Awards: Editor's Highlights in Journal of Applied Physics (2018) and Nanotechnology (2015) Best Innovative Idea, Second International R&D Award of Iran (2012) Distinct Graduate Student, University of Tehran Visiting Study Scholarship, Instituto de Microelectronica de Madrid (2010) M.Sc. First Class Student (top among 15) He serves as Unit Director for Mechatronics II and Electronics II modules and supervises BEng/MEng research projects. Active research grants include Royal Society funding (2024–2026) for cancer cell biomechanics and an ongoing 2022–2026 project on solid-liquid interface dynamics.

• Embedded Systems
• Hardware-Software Co-Design
• Invasive Computing
• +7 more

Prof. Dr.-Ing. Jürgen Teich is a full Professor and Chair for Hardware-Software Co-Design at the Department of Computer Science, Friedrich Alexander University Erlangen-Nuremberg (FAU). He serves as Head of Department Computer Science and Vice Dean of the Technical Faculty since August 2024, and has been Speaker of the FAU Research Center Embedded System Initiative (FAU ESI) since 2023. His educational background includes: Diploma degree in Electrical Engineering, University of Kaiserslautern (1989) Dr.-Ing. degree in Electrical Engineering, University of Saarland (1993) Habilitation (PD Dr.-Ing.) entitled "Synthesis and Optimization of Digital Hardware/Software Systems" (1996) Prof. Teich's research focuses on Embedded Systems , Invasive Computing , Hardware-Software Co-Design , and Reconfigurable Computing . His work spans from theoretical foundations to practical implementations, with particular emphasis on resource-constrained systems, many-core architectures, and energy-efficient computing. He has pioneered research in invasive computing paradigms that enable more efficient use of many-core processors by allowing applications to dynamically claim resources. His recent publications reveal a strong trend toward energy-efficient AI deployment on embedded devices , security of embedded systems , and novel memory technologies . There's a clear focus on practical implementations of machine learning on microcontrollers (TinyML), hardware acceleration for data processing, and innovative approaches to power management in self-powered systems. Among his notable scientific awards are: IEEE Fellow (since 2018) Member of Academia Europaea, Section Informatics (since 2011) Member of the National Academy of Science and Engineering (acatech) (since 2018) Member of the German Society of Humboldtians (since 2021) Prof. Teich has been Principal Investigator for numerous DFG-funded projects including SFB/Transregio 89 "Invasive Computing" (2010-2022), SFB 694, and multiple priority programs. He has coordinated large collaborative research efforts across Germany and internationally, with significant funding from DFG and other sources. His research group has produced influential work in embedded systems design and co-design methodologies. He leads the Hardware-Software Co-Design research group at FAU, which focuses on innovative approaches to embedded system design, invasive computing architectures, and efficient implementation of machine learning on resource-constrained devices. The group maintains strong collaborations with industry partners including Intel, Xilinx, and automotive companies.

• Nuclear Energy Systems
• Accelerator Technology
• Reactor Technology
• +3 more

Prof. Dr. Hüseyin Yapıcı is a faculty member in the Department of Mechanical Engineering at Başkent University . His research focuses on Nuclear Energy Systems , Accelerator Technology , and Thermodynamics . Nuclear Reactor Design Energy Systems Optimization Heat Transfer Analysis His work involves numerical simulations , neutronic analysis , and nuclear waste transmutation . Recent publications highlight three-dimensional power density modeling in accelerator-driven systems and tritium production studies. Prof. Yapıcı has supervised numerous students, including Gizem Bakır , Alper Buğra Arslan , and Büşra Durmaz , across diverse projects from fusion-fission hybrids to renewable energy systems .

• Digital Circuits
• Digital Signal Processing
• Machine Learning
• +10 more

Maurizio Martina is a Full Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino. He is a member of the Interdepartmental Center PEIC - Power Electronics Innovation Center and serves as an Associate Editor for the IEEE Transactions on Circuits and Systems I (2018-2023). His research focuses include: Digital circuits and signal processing Machine learning hardware architectures RISC-V extensions and post-quantum cryptography VLSI design for edge computing and IoT Recent publications emphasize cryptographic hardware implementations (CHIMERA, Keccak co-processors), RISC-V integration methodologies, and privacy-preserving neural network frameworks. His work spans VLSI architectures for video processing, bio-inspired electronics, and error correcting codes, with applications in cybersecurity, robotics, and biomedical systems. Scientific Recognition : Premio Nazionale Innovazione (2013) Premio dei Premi (2014) He supervises 12 PhD students across cycles 35-40 in Electrical, Electronics and Communications Engineering, including: Valeria Piscopo (2024-in progress) Alessandra Dolmeta (2022-in progress) Luigi Giuffrida (2022-in progress) Walid Walid (2019-2023) As part of the VLSILAB Group , his research explores hardware accelerators for machine learning, post-quantum cryptography on RISC-V, and bio-inspired embedded systems. Teaching activities include courses on Integrated Systems Architecture and Hardware & Wireless Security at Politecnico di Torino and Università di Pavia.

• Control Technology
• Distributed Optimization
• Machine Learning
• +4 more

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

Koroush ShirvanView profile

Associate Professor

• Nuclear Reactor Design
• Advanced Nuclear Fuels
• Small Modular Reactors
• +6 more

Koroush Shirvan is the Atlantic Richfield Career Development Professor in Energy Studies and a tenured faculty member in MIT's Department of Nuclear Science and Engineering within the School of Engineering. Joined in July 2017, he directs the Reactor Technology Course for Utility Executives and leads the Fission Materials in Extreme Environments Lab. His work bridges nuclear engineering with practical industrial applications for decarbonization. His research focuses on reactor design economics, materials testing under irradiation, nuclear safety, and boiling heat transfer. He accelerates innovations in nuclear fuels, small modular reactors, and space propulsion through multi-scale physics integration. Current projects include accident-tolerant fuels, high-temperature materials for microreactors, and AI-driven optimization of reactor systems. His approach combines experimental irradiation testing at MITR with advanced computational modeling. Recent publications reveal strong trends toward economic nuclear deployment via advanced fuel technologies and small modular reactors. AI/ML applications dominate optimization research, particularly for core reload and uncertainty quantification. Materials science under extreme conditions remains central, with growing emphasis on space nuclear applications and horizontal reactor configurations for cost reduction. His scientific recognition includes: Nuclear News 40 under 40 (2024) American Nuclear Society Landis Young Member Engineering Achievement Award (2023) American Nuclear Society Reactor Technology Award (2022) Teaching responsibilities span Sustainable Energy (22.811/081), Graduate Reactor Physics, and Nuclear Design courses. Research grants support experimental programs at MIT Reactor Lab and computational frameworks for reactor-to-repository analysis. He mentors students through senior design projects and graduate research in nuclear fuel cycles. He directs the Fission Materials in Extreme Environments Lab and co-leads MIT's Space Nuclear initiative with AeroAstro. The team conducts irradiation experiments using MITR's high-temperature hydrogen flow capabilities and advanced diagnostics for post-irradiation examination. Current thrusts include nuclear thermal rocket materials testing and fission surface power development for lunar/Mars missions.

Marat I. LatypovView profile

Assistant Professor

• Materials Science
• Computational Materials Science
• Materials Informatics
• +4 more

Marat I. Latypov serves as Assistant Professor in the Department of Materials Science and Engineering at the University of Arizona's College of Engineering. He is also a member of the Applied Mathematics Graduate Interdisciplinary Program and leads the Materials Informatics Lab. His research spans computational materials science, sustainable alloy design, and machine learning applications for materials development. Dr. Latypov holds a PhD in Materials Science and Engineering from Pohang University of Science and Technology (POSTECH, South Korea, 2014) and a Dipl.-Ing. in Engineering Physics from Ufa State Aviation Technical University (Russia, 2011). His postdoctoral training included appointments at Georgia Tech/CNRS in France and the University of California, Santa Barbara. His research focuses on materials informatics , physics-informed machine learning , and sustainable structural alloys . Key methodologies include graph neural networks for polycrystal mechanics, vision transformers for microstructure representation, and adaptive experimental design for materials optimization. Recent work emphasizes circular economy applications through construction waste recycling and copper mine tailings valorization. Analysis of his publication record reveals strong emphasis on computational microstructure-property linkages (35% of recent work), machine learning for materials design (30%), and sustainable materials processing (25%), with growing integration of large language models for materials knowledge extraction. NSF CAREER Award (2025) : For damage control in recycled aluminum alloys ISTI Distinguished Faculty Scholar (2024) : At Los Alamos National Laboratory Novelis Hackathon First Prize (2021) : Computer vision application Acta Materialia Outstanding Reviewer (2018) Young Researcher Award (2017) : NanoSPD7 Conference Dr. Latypov advises PhD students including Herbold Fellow Zhuocheng Huang and leads projects funded by NSF and the Grantham Foundation. Current initiatives include chalcopyrite leaching optimization for copper mining and graph neural network development for fatigue prediction. His Materials Informatics Lab maintains collaborations with Los Alamos National Laboratory, MIT, and industry partners including Novelis. The lab operates at the intersection of metallurgy , machine learning , and high-performance computing , with capabilities spanning deep learning, Bayesian inference, and cloud-based computational infrastructure. Recent news highlights participation in CODAS-HEP summer school and publication of vision transformer work in Acta Materialia.

Guiru Nash LiuView profile

Professor

• Materials Science
• Metallurgy
• Corrosion
• +4 more

Guiru Nash Liu is a Global Professor in the Department of Materials Science and Engineering at the University of Arizona. She holds a PhD from Illinois Institute of Technology and has prior industrial experience as a senior experimental metallurgist at Progress Rail (Caterpillar Company) and as an adjunct professor at Illinois Institute of Technology. BS: Tianjin University, P.R. China MS: University of Southern California PhD: Illinois Institute of Technology (Materials Science and Engineering) Her research focuses on materials science and metallurgy , with specialization in corrosion, fatigue analysis, microstructural characterization, and alloy development . She has contributed to understanding fatigue failure in metallic components, environmental effects on crack propagation, and corrosion behavior in extreme conditions. Guiru Nash Liu's publications highlight expertise in corrosion kinetics, sintering mechanisms, alloy performance, and fatigue mechanics , particularly for titanium, copper, and steel alloys used in locomotive engines and aerospace applications. Fellow of ASM International (2020) Allan Ray Putnam Service Award (ASM International, 2022) Caterpillar CEO Award (2022) She has authored over 150 internal publications and 14 peer-reviewed works, served as a reviewer for the Journal of Materials Science and Journal of Metallography, Microstructure and Analysis , and was a founding member of the ASM International Failure Analysis Society.

Kibret MequanintView profile

Professor

• Biomaterials
• Tissue Engineering
• Regenerative Medicine
• +6 more

Dr. Kibret Mequanint is a full Professor at Western University's Department of Chemical and Biochemical Engineering, with cross-appointments in Biomedical Engineering. Holding a PhD from University of Stellenbosch and postdoctoral experience at Technical University of Darmstadt and McMaster University, his research bridges polymer science, materials engineering, and life sciences with applications in Biomaterials , Tissue Engineering , and Regenerative Medicine . His work spans both fundamental and translational research in cell-material interactions , polymer biomaterial design , and therapeutic radiation dosimeters , with technologies transferred to commercial applications. Leading scholar and educator with awards from NSERC, CIHR, and Western University Fellow of: American Institute for Medical and Biological Engineering (AIMBE), Ethiopian Academy of Sciences, International Union of Societies for Biomaterials Science and Engineering, Canadian Academy of Engineering Extensive editorial and panel service for NSERC, CIHR, and international journals His research program has produced over 170 refereed publications, focusing on conductive hydrogels , bioadhesives , and vascular tissue engineering . Recent work on endoscopy-deliverable bioadhesives and snake venom-derived hemostatic gels has attracted global media attention. He has served in leadership roles at the Canadian Biomaterials Society and university governance bodies including Senate and Board of Governors.