Find Academic Supervisors

• Soft Matter Physics
• High-resolution Imaging
• Nano-science
• +3 more

Professor Ashley Cadby is a faculty member in the School of Mathematical and Physical Sciences at the University of Sheffield, holding the position of Professor of Soft Matter Physics within the Department of Physics and Astronomy. Her research focuses on cutting-edge imaging techniques applied to biological systems. Her research interests center on Soft Matter Physics , High-resolution Imaging , and Nano-science , with particular emphasis on super-resolution microscopy techniques applied to biological systems. Her work bridges physics, biology, and nanotechnology to investigate cellular structures and processes at unprecedented resolution. Analysis of her recent publications reveals a consistent focus on developing and applying advanced imaging methodologies, particularly super-resolution and correlative microscopy techniques. Her research spans bacterial cell wall architecture, sperm biology, protein dynamics, and nanoscale biological structures, demonstrating interdisciplinary applications across microbiology, reproductive biology, and cellular biophysics. Professor Cadby contributes to teaching through undergraduate courses including PHY 101 Tutorials, PHY 113/114 Computing Laboratory, PHY 245 Materials, and specialized courses in Bio-Physics (PHY 411/412) and Mechanistic Biology (PHY 6120). As Biological Safety Officer and member of the IOP Nano-science group and PARC consortium, she maintains active professional engagement. She leads the Biological Physics Group research team, focusing on innovative imaging approaches to solve complex biological problems through physical methodologies.

Mohammed El-KebirView profile

Associate Professor

• Cancer Genomics
• Computational Biology
• Combinatorial Optimization
• +4 more

Mohammed El-Kebir is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC) , with affiliate appointments in Electrical & Computer Engineering, the Institute of Genomic Biology, and the National Center for Supercomputing Applications. His research focuses on combinatorial optimization algorithms for fundamental challenges in cancer genomics and computational biology , particularly tumor phylogenetics , single-cell sequencing data analysis , and metastasis migration history modeling . PhD in Computer Science, Centrum Wiskunde & Informatica (Netherlands) and VU University Amsterdam (2015) Postdoctoral training at Brown University and Princeton University (2014-2017) El-Kebir's work addresses intra-tumor heterogeneity by developing mathematical models to reconstruct evolutionary trees of cancer mutations from mixed sequencing data. His lab has pioneered methods like SPhyR and MACHINA for phylogeny inference under data limitations, and PhyDOSE for optimizing single-cell sequencing experiments. His research bridges computational challenges with clinical implications for personalized medicine and therapy resistance prediction . Key contributions include: Theoretical foundations for cancer phylogenetic problems (hardness proofs, algorithm design) Single-cell sequencing algorithms accounting for data sparsity and errors Migration graph frameworks for metastasis analysis Interactive tools like ClonArch and CNAViz for tumor data visualization El-Kebir has received multiple accolades including the NSF CAREER Award (2021) and CRII Award (2019) . His group has produced 15+ peer-reviewed publications since 2020, with student collaborators securing prestigious fellowships like the NSF GRFP . He actively contributes to conference program committees (RECOMB, ISMB, WABI) and teaches graduate-level courses in Bioinformatics and Computational Cancer Genomics .

• Control and optimization
• Epidemic processes over networks
• Network inference
• +5 more

Carolyn L Beck is a Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign . She holds affiliations with multiple departments, including Electrical and Computer Engineering (since 2014) and Mechanical Science and Engineering (since 1999). Beck is also a Research Professor at the Coordinated Science Laboratory (since 2019) and has served as Associate Head for Undergraduate Programs since 2020. Education : Ph.D. in Electrical Engineering from California Institute of Technology M.S. in Electrical & Computer Engineering from Carnegie Mellon University B.S. in Electrical & Computer Engineering with a Physics minor from California State Polytechnic University Research Interests focus on control and optimization , epidemic processes over networks , network inference , and dynamic network data clustering . Her work spans mathematical systems theory to real-world applications in bioengineering and smart grid optimization . Article Trends show a progression from system identification and network inference to complex epidemic modeling over time-varying networks and grid optimization. Recent works emphasize finite-sample analysis , distributed subgradient methods , and multi-layer contagion dynamics , reflecting her expertise in control theory and network science . Scientific Awards : IEEE Fellow (2023) Arthur Davis Faculty Scholar Award (2016) ONR Young Investigator Award (2001-2004) NSF CAREER Award (1998-2003) ORAU Junior Faculty Award (1997) Alcoa Foundation Award (1997) Advising and Grants include mentoring former PhD students like Puneet Sharma (AIMBE Fellow) and Philip Pare' (Purdue ECE) . Her research has been funded by the NSF , ONR , and IEEE , with a $500,000 grant for wind turbine efficiency. Labs and Teams : Beck is affiliated with the Coordinated Science Laboratory and collaborates with interdisciplinary teams in bioengineering , network control , and grid optimization . She also contributes to IEEE as a Guest Editor and Associate Editor .

Richard Y. ZhangView profile

Assistant Professor

• Optimization
• Machine Learning
• Low-Rank Optimization
• +3 more

Richard Y. Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. He earned his PhD in Electrical Engineering & Computer Science from MIT in 2017 and completed a postdoc at UC Berkeley's Industrial Engineering & Operations Research Department (2017-2019). His research bridges optimization and machine learning, focusing on low-rank optimization as both a theoretical framework for signal recovery and a computational tool for large-scale algorithms in power systems and neural networks. Current research explores nonconvex optimization landscapes, with applications in unsupervised learning, adversarially robust neural networks, and electric grid state estimation. His group has developed algorithms for dictionary learning, semidefinite programming relaxations (SDP-CROWN), and preconditioned optimization methods. Key collaborators include researchers from MIT, UC Berkeley, and the Power Systems Engineering Research Center (PSERC). NSF CAREER Award (2021) Area Chair: NeurIPS (2021-present), ICML (2023-present), ICLR (2024-present) Advising: Hong-Ming Chiu, Iven Guzel, June Hou; Alumni: Gavin Zhang (PhD '24, now at Meta) His recent work includes groundbreaking contributions to the theoretical understanding of low-rank matrix recovery and practical applications in power system optimization. He teaches ECE 330 (Power Circuits & Electromechanics) and ECE 530 (Large-Scale System Analysis), emphasizing the interplay between theory and real-world implementation.

• Population Ecology
• Epidemiology
• Infectious Disease Dynamics
• +5 more

Daniel Haydon is a Professor of Population Ecology and Epidemiology at the University of Glasgow, affiliated with the School of Biodiversity, One Health and Veterinary Medicine. He joined the university in 2004 after completing a PhD at the University of Texas at Austin and postdoctoral positions at Oxford, UBC, Edinburgh, and Guelph. From 2010–2022, he served as Director of the Institute of Biodiversity, Animal Health and Comparative Medicine, which later merged to form the current School. He convenes the School's Fellowship Committee and leads the College Futures theme in Planetary Health. His research integrates quantitative approaches in ecology and epidemiology. Key interests include: Community structure/function, stability in food webs/competitive networks Spatial population dynamics, movement models, and population viability Wildlife/livestock/zoonotic disease epidemiology using pathogen genomics Innovative seroprevalence analysis and outbreak size prediction He collaborates extensively with the Wellcome Trust's Afrique One initiative to build One Health capacity in sub-Saharan Africa. His recent articles (2023–2025) focus on: Disease dynamics (rabies, brucellosis, RVF, leptospirosis) Ecological modeling methodologies (observation processes, isotope geolocation) Agroecology and conservation (cocoa farms, pollinator habitats) One Health economics (vaccine valuation, farmer wellbeing) This reflects sustained engagement with complex disease systems, molecular tools, and interdisciplinary approaches across Africa and globally. Daniel actively supports academic development as Fellowship Committee convenor and contributes to the University's Planetary Health strategy. He maintains collaborations across continents, particularly in Tanzania and Ghana.

Benoît SAGOTView profile

Professor

• Aerosol Science
• Thermophoresis
• Particle Separation
• +5 more

Benoît SAGOT is a Professor at ESTACA (Higher School of Aeronautical and Automotive Engineering Construction), Campus Paris Saclay, where he serves as a Teacher-researcher in the School of Mobility Systems and Complex Engineering within ESTACA'Lab. His work focuses on aerosol science, thermophoresis, and particle separation technologies, with applications in automotive engineering and pollution control systems. His educational background includes: Accreditation to Supervise Research (HDR) from University of Paris-Est (2020) PhD in Process Engineering and Sustainable Development from University of Technology of Compiègne (2010) DEA in Fluid Mechanics and Energy from INPL (1993-1994) Engineering degree from ENSEM (National School of Electricity and Mechanics, Nancy) (1991-1994) Dr. SAGOT's research centers on thermophoresis, aerosol science, and particle separation technologies. His work investigates the behavior of particles in gas flows, with particular focus on thermophoretic transfer mechanisms, cyclone separator efficiency, and applications for reducing particulate emissions from internal combustion engines. His research has significant implications for automotive emissions control and air quality improvement. His publication record shows a consistent focus on thermophoresis and particle separation, with recent work expanding into respiratory droplet characterization. The research demonstrates a progression from fundamental thermophoretic principles to practical applications in automotive engineering, particularly in blow-by gas cleaning systems and engine emissions control. Dr. SAGOT holds several professional leadership positions: Board member of ASFERA (French Aerosol Research Association) since 2017 Member of GDR "Suies" (Soot Research Group) since 2014 Member of ISO/TC 22/SC 34/WG 11 for standardizing test protocols for crankcase gas filters Session chair at European Fluid-Particle Separation Congress (2018) Session chair at French Aerosol Congress (2019) As a research supervisor with HDR accreditation, Dr. SAGOT leads projects including CAPNAV and EMINAV (ADEME CORTEA and AQACIA type projects). His work bridges academic research and industrial applications in automotive engineering, with strong connections to industry standards through his ISO committee work. Dr. SAGOT is actively involved with ESTACA'Lab, where he conducts research on thermophoretic phenomena and particle separation technologies. His laboratory work focuses on experimental validation of theoretical models for particle deposition and the development of instrumentation for aerosol characterization, particularly for automotive applications.

Philippe CiblatView profile

Professor

• Statistical Signal Processing
• Digital Communications
• Resource Allocation
• +6 more

Philippe Ciblat is a Full Professor at Institut Polytechnique de Paris , affiliated with the Communications and Electronics Department (Comelec) and the Information Processing and Communication Laboratory (LTCI) . He obtained his Engineering Degree from Télécom Paris (1996), DEA in Automatic Control from Université Paris-Saclay (1996), PhD from Université Gustave Eiffel (2000), and HDR diploma (2007). After a postdoctoral fellowship at Université de Louvain (2001), he joined Télécom Paris as Associate Professor in 2001 and was promoted to Full Professor in 2011. Research Activities: His research spans three core areas: Statistical/Digital Signal Processing (blind equalization, frequency estimation, distributed estimation) Signal Processing for Communications (OFDM synchronization, UWB localization, cooperative communications) Resource Allocation (HARQ optimization, power allocation, distributed networks) Scientific Leadership: He has served as Associate Editor for IEEE Transactions on Signal and Information Processing over Networks (2018-2022), Senior Area Editor for IEEE Transactions on Signal Processing (2011-2012), and held editorial roles in IEEE Communications Letters and Transactions on Signal Processing. He chairs technical committees at EUSIPCO, SPAWC, and IEEE conferences. PhD Supervision: He has mentored 23 PhD students, including Current: Nils Reynaud (deep reinforcement learning for wind farms), Abdelghani Ghanem (multi-agent driving behavior), Sylvain Nerondat (deep reinforcement learning for ad hoc networks) Former: Yue Bi (distributed computing), Hakim Hafidi (robust graph ML), Arthur Louchart (nonlinear satellite systems), Vincent Corlay (lattice decoding), Alix May (optical network monitoring) Grants & Collaborations: Currently leads projects with TotalEnergies (2025-2028), PEPR Twinfarms (2025-2028), and ANR SUCCEN (2025-2027). Formerly secured ANR, Marie-Curie, and CIFRE grants with Thales, Nokia, Mitsubishi, and Huawei. Labs & Teams: Affiliated with LTCI (Signal, Statistics and Learning team) and has conducted sabbaticals at Technische Universität Berlin, Rutgers University, and Universidad Nacional de Colombia. Actively participates in international conferences as TPC member and reviewer.

• Biocatalysis
• Enzyme Technology
• Analytical Chemistry
• +2 more

Carl Grey is a Senior Lecturer and Office Director at the Division of Biotechnology and Applied Microbiology within Lund University's Faculty of Engineering (LTH). He is an active member of the LTH Profile Area: Food and Bio, contributing to research at the intersection of biotechnology and food science. His academic profile includes substantial research output with expertise validated by ORCID (0000-0001-5701-7303) and ongoing collaborations. His research focuses on bioprocess technology and enzyme applications, with particular emphasis on: Biocatalytic systems development and enzyme engineering Analytical methods for complex biological samples Sustainable food production technologies Microbial biotechnology and biomolecular analysis Bioremediation and environmental biotechnology applications Analysis of Dr. Grey's recent publications reveals a strong interdisciplinary approach combining biotechnology with computational methods, food science, and environmental applications. His work consistently addresses fundamental biochemical processes while developing practical applications in sustainable bio-production. As a supervisor, Dr. Grey mentors PhD students in projects related to biosynthesis pathways including: Bio-synthesis of capsaicinoids in yeast systems Whole-cell biocatalysis for chiral amine production He also leads research projects such as the FORCE Center for Food Resilience and Competitiveness (2024-2028), focusing on sustainable food systems. Dr. Grey operates within Lund University's biotechnology research infrastructure, collaborating with specialized labs including the Microbial Flow Cytometry facility. His team works on developing innovative bioprocessing techniques and analytical methodologies.

• Graph Algorithms
• Network Analysis
• Scientific Workflow Scheduling
• +3 more

Henning Meyerhenke is a Professor at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin. He serves as Deputy Director of Teaching and Studies, focusing on graph algorithms , network analysis , and scientific workflow scheduling . His research spans complex systems modeling and parallel computing. Ranks: Professor Institution: Humboldt University of Berlin Research Interests include energy-efficient computing, heterogeneous systems, and scalable graph algorithms. Recent work explores network sparsification , workflow mapping , and graph robustness under edge deletions. His group also investigates climate science applications via climate networks . Publication Trends reflect advancements in parallel algorithms , distributed systems , and adaptive scheduling for scientific workflows. Key subfields include memory-aware computing , harmonic centrality , and edge sampling techniques. Student Advising : Michael Piechotta (PhD candidate, defended September 15, 2025)

Kurt AnstreicherView profile

Professor

• Mathematical Programming
• Optimization Expertise
• Interior-Point Algorithms
• +3 more

Kurt Anstreicher is a Professor of Business Analytics at the University of Iowa's Tippie College of Business, with complementary appointments in the Department of Computer Science and Department of Industrial Engineering. He holds the Gary C. Fethke Chair in Leadership. His research focuses on mathematical programming, optimization, interior-point algorithms, and nonlinear programming. Anstreicher earned his Ph.D. from Stanford University in 1983 and a B.A. from Dartmouth College in 1978. Research interests include optimization theory and methods, particularly in nonconvex programming, convex relaxations, and algorithm design. His work addresses challenges in quadratic programming, semidefinite optimization, and global optimization techniques. Recent contributions explore applications in trust-region subproblems, copositive cones, and maximum-entropy sampling. Publications span topics like convex hull representations, Kronecker product constraints, and spherical codes. His work bridges theoretical advancements and practical computational methods, with implications for operations research, data analysis, and engineering systems.

Virgilio A. CentenoView profile

Professor

• Power Systems
• Phasor Measurement Units (PMU)
• Grid Cybersecurity
• +4 more

Virgilio A. Centeno is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research focuses on power systems, phasor measurement units (PMUs), grid cybersecurity, and smart grid technologies. He holds a Ph.D. from Virginia Tech (1995) and has contributed extensively to advancing power system resilience, wide-area monitoring, and renewable energy integration. His work emphasizes practical applications such as GPS-spoofing detection, cascading failure prevention, and adaptive control strategies for modern grids. Research interests include synchronized sampling, power systems as critical infrastructure, microcontroller applications, and power systems education. His recent publications highlight innovative methods for PMU data recovery, event classification, and adaptive voltage security in renewable-rich grids. Collaborations involve developing testbeds for cyber-physical systems and creating realistic synthetic power networks to model interdependencies. No awards or honors are explicitly listed in the provided text. His teaching interests align with his research areas, and he remains affiliated with Virginia Tech’s research groups. Ongoing work includes proactive islanding strategies, Gaussian process-based dynamic inference, and optimal distribution network partitioning for resilience enhancement.

Mario MarchandView profile

Professor

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +3 more

Mario Marchand is a retired Professor in the Department of Computer Science and Software Engineering at Université Laval, Canada. His research focuses on machine learning theory, explainable AI, and computational biology. He has contributed to foundational work in PAC-Bayesian analysis, generalization bounds, and kernel methods. Marchand is affiliated with the Computer Vision and Systems Laboratory and the GRAAL research group. Though no longer supervising graduate students, his recent work addresses challenges in algorithmic stability, meta-learning, and feature attribution consensus. His publications span topics from neural network optimization to applications in bioinformatics and drug discovery. Research Interests: Machine Learning Theory, Explainable AI, Bioinformatics, Kernel Methods, Domain Adaptation, and Statistical Learning. Key Contributions: PAC-Bayesian risk bounds, decision tree analysis, and algorithms for mixture models. His work bridges theoretical guarantees and practical applications in healthcare and computational biology. Professional Activities: Taught courses including IFT-7002 (Foundations of Machine Learning) and contributed to international conferences. His methods are used in biomarker discovery and peptide design for drug development. Recent trends in his articles emphasize explainable AI (XAI) and resolving feature attribution disagreements, alongside foundational studies on generalization in meta-learning and multi-source domain adaptation. His collaborative projects include predicting molecular properties and advancing neural network stability.

Diane LeeView profile

Research Fellow

• Epithelial Cell Biology
• Equine Asthma
• Bovine Tuberculosis (BTB)
• +3 more

Diane Lee is a Research Fellow B in Pathology and Infectious Diseases at the University of Surrey's School of Veterinary Medicine. She holds a PhD, MSc, and BSc (Hons) and specializes in ex vivo/in vitro cell biology with a focus on drug discovery and pre-clinical research. Her roles include managing the Impact Acceleration Account and KE (MRC & PhD/ECR) and serving on the NASPA Sub-committee. Education: PhD in Pharmacy and Biomolecular Sciences from the University of Brighton (2015), MSc and BSc from unspecified institutions. Research Interests : Equine Asthma: Developing nasal brush models to study mechanisms of asthma and Notch signaling in goblet cell hyperplasia. Bovine Tuberculosis (BTB): Created a bovine alveolus model using co-culture of alveolar and endothelial cells to study host-pathogen interactions, reducing animal use. 3Rs Principles: Championing replacement/refinement/reduction of animal experiments through in vitro models. Publications : Over 15 peer-reviewed articles, including work on bovine lung-on-chip models, equine asthma nasal models, and HSP70 inhibitors. Collaborations : Works with NIBSC, Pirbright Institute, and APHA. Active in developing organ-on-chip technologies and validating cell culture models for respiratory diseases. Labs/Teams : Leads projects on equine respiratory models and bovine tuberculosis at the University of Surrey, with a focus on translational veterinary medicine.

Dana RandallView profile

Professor

• Theoretical Computer Science
• Randomized Algorithms
• Statistical Physics
• +4 more

Dana Randall is the ADVANCE Professor of Computing and an Adjunct Professor of Mathematics at Georgia Institute of Technology. She serves as the Associate Dean for Access and Advancement in the College of Computing, Co-Executive Director of the Institute for Data Engineering and Science (IDEaS), and previously directed the Algorithms and Randomness Center (ARC). Her research bridges theoretical computer science, discrete mathematics, and statistical physics, focusing on randomized algorithms and stochastic processes. Education: A.B. in Mathematics, Harvard University (1988) Ph.D. in Computer Science, University of California, Berkeley (1994) Research Interests: Randomized algorithms and Markov chain Monte Carlo methods Computational problems in statistical physics Combinatorics, distributed algorithms, and collective computation Awards & Honors: Fellow of the American Mathematical Society (AMS) National Associate of the National Academies Former Sloan Fellow and NSF Career Award recipient Grants & Leadership: Principal Investigator on a $6.25M ARO MURI grant on collective emergent behavior (2019–2024) Co-founder and Co-Executive Director of IDEaS (2016–2019) Directed the Algorithms and Randomness Center (2014–2016) Labs & Teams: Active in the Algorithms and Randomness Center (ARC) and Institute for Data Engineering and Science (IDEaS), focusing on interdisciplinary research in algorithms, data science, and computational theory.

• Computational Structural Biology
• Statistical Machine Learning
• DNA Shape Prediction
• +4 more

Remo Rohs is a tenured Professor of Quantitative and Computational Biology, Chemistry, Physics and Astronomy, Computer Science, and Biomedical Engineering at the University of Southern California Dornsife College. He leads the Rohs Lab, focusing on integrating genomics and structural biology to uncover molecular mechanisms in gene regulation. Chair, Department of Quantitative and Computational Biology (2021–2024) Section Head, Quantitative and Computational Biology (2018–2020) Full Professor with Tenure at USC (2016–present) Research Interests center on: Computational structural biology DNA shape prediction Protein-DNA recognition mechanisms Epigenetic effects on binding specificity RNA structure modeling High-throughput genomic analysis His work combines artificial intelligence , molecular simulations , and experimental validation to study transcription factors and chromatin structure. Recent publications highlight deep learning methods for DNA/RNA structure prediction and database development for molecular interactions. Scientific Awards include: Sloan Research Fellowship (2013–2017) Multiple RECOMB/ISCB Top-Paper Awards (2012–2017) American Chemical Society OpenEye Outstanding Junior Faculty Award (2015–2016) USC Mentoring Award (2015–2016) He has supervised 13 PhD graduates and published 92+ peer-reviewed papers . His lab develops tools like DNAproDB and TFBSshape for structural analysis of nucleic acid complexes.