Find Academic Supervisors

Laurent CharlinView profile

Associate Professor

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

Roles & Affiliations Associate Professor at HEC Montréal (Department of Decision Sciences) with adjunct ties in Computer Science. Holder of a Canada CIFAR AI Chair. Member of MILA, the Chair Data Science for Real-Time Decision Making, and the Centre de recherche en mathématiques. Education Ph.D. in Computer Science (University of Toronto), MMath (Computer Science, University of Waterloo). Research Interests Focuses on machine learning applications in decision-making, including reinforcement learning for traffic control, energy systems, and recommender systems. Specializes in topics like continual learning, graph-based methods, and operations research integration. Develops foundational models such as collaborative topic Poisson factorization and neural combinatorial optimization approaches. Publications Overview Recent work includes reinforcement learning for sustainable energy use (2025), traffic signal control innovations (2024-2022), and foundational contributions like CTPF (2014). His research bridges theory and application, emphasizing scalable solutions for complex systems. Awards & Recognition Canada CIFAR AI Chair in Artificial Intelligence Advising & Grants Supervised over 30 students across PhD and Master's programs, with notable projects in traffic optimization, electricity pricing, and toxic content analysis. Active in grant-funded research through collaborations with industry partners like Beneva and academic networks like MILA. Labs & Collaborations Core member of MILA and affiliated with HEC's decision science research groups. Leads projects in real-time decision-making and data science applications across domains.

• Algorithmic Game Theory
• Mechanism Design
• Approximation Algorithms
• +6 more

Itai Feigenbaum is an Associate Professor in the Department of Computer Science at Lehman College and the Computer Science Program at the Graduate Center, both part of the City University of New York (CUNY) system. His academic career focuses on bridging theoretical computer science with practical applications in economic and social systems. Dr. Feigenbaum's educational background includes: Ph.D. in Operations Research from Columbia University (2016) M.Sc. in Operations Research from Columbia University (2012) B.Sc. in Mathematics from Rutgers University-New Brunswick (2011), with minors in Computer Science and Operations Research His research spans multiple interconnected domains at the intersection of computer science, economics, and operations research. Dr. Feigenbaum specializes in algorithmic game theory and mechanism design , developing theoretically sound yet practically applicable solutions to complex allocation problems. His work in causal inference has produced significant contributions to understanding the theoretical foundations of causal discovery algorithms. In combinatorial optimization , he has tackled challenging problems in kidney exchange and school choice systems, creating algorithms that balance efficiency, fairness, and strategic considerations. His recent work increasingly integrates machine learning techniques with traditional optimization approaches, particularly in the causal inference space. Analysis of Dr. Feigenbaum's publication record reveals a consistent focus on strategic decision-making in constrained environments. His work demonstrates a progression from theoretical foundations in mechanism design to increasingly complex real-world applications, particularly in healthcare (kidney exchange) and education (school choice). The recent emphasis on causal inference represents a natural extension of his expertise in algorithmic decision-making under uncertainty. His publications appear in top venues across computer science, operations research, and economics, reflecting the interdisciplinary nature of his contributions. Dr. Feigenbaum has established productive collaborations with researchers across multiple institutions, particularly with colleagues at Columbia University (where he completed his PhD under Jay Sethuraman) and various AI research labs. His work on causal inference has involved collaborations with researchers from major tech companies, as evidenced by publications like the Salesforce CausalAI Library framework.

Sijbren OttoView profile

Professor

• Systems Chemistry
• Origin of Life
• Synthetic Biology
• +5 more

Sijbren Otto is a Professor of Systems Chemistry at the Faculty of Science and Engineering , University of Groningen. His research bridges chemistry and biology through pioneering work on chemical evolution and self-replicating molecular systems . Key contributions include demonstrating how synthetic molecules can evolve Darwinian traits and developing life-emulating systems with replication, metabolism, and compartmentalization. Education: PhD in Chemistry (1998) from the University of Groningen, Royal Society University Research Fellow (2001–2009) Research Interests: Focus on systems chemistry , self-replication , foldamers , and dynamic combinatorial chemistry , aiming to synthesize life de novo . Article Trends: Recent work explores light-driven molecular systems , error-correcting replicators , and DNA-based programmable networks , integrating dissipative processes and stochastic emergence . Awards: James Flack Norris Award (2021) ERC Synergy Grant (2023) KNAW Membership (2020) Vici Grant (2013) Royal Society of Chemistry Fellowship Grants: Recipient of multiple ERC grants (2011, 2017, 2023) and NWO funding for Functional Molecular Systems (2013–2023). Labs: Leads the Otto Research Group at the Stratingh Institute, focusing on artificial life and evolutionary molecular networks .

Daniel SussmanView profile

Assistant Professor

• Probability and Statistics
• Network Analysis
• Graph Theory
• +3 more

Dr. Daniel Sussman is an Assistant Professor at Boston University, affiliated with the Probability and Statistics research group. He also serves as the Associate Director of the Program in Statistics. His work focuses on statistical methodologies for network analysis, including graph matching, latent variable models, and causal inference under network interference. Dr. Sussman develops computational tools for analyzing complex networks, such as the iGraphMatch R package for graph matching analysis. His research bridges theoretical foundations with practical applications in neuroscience, epidemiology, and computational social science. Key research interests include network dynamics, stochastic processes on graphs, and statistical estimation in noisy network data. He explores geometric and algebraic properties of random graphs, as well as scalable algorithms for large network datasets. His contributions span theoretical results (e.g., asymptotic consistency of embeddings) to applied methods (e.g., unbiased estimation in exposure models). Dr. Sussman’s publications highlight advancements in graph matching techniques, including matched filter approaches and multi-scale heat diffusion methods. His work emphasizes robustness to noise and scalability for real-world networks. While no explicit awards are listed, his contributions reflect significant impact in statistical network analysis. His professional role as Associate Director involves overseeing statistical training programs and curricula. Though no advising details are provided, his research group likely supports graduate students focused on network science and computational statistics.

William PetterssonView profile

Researcher

• Operations Research
• Integer Programming
• Graph Theory
• +6 more

Dr. William Pettersson is a Researcher at the School of Computing Science, University of Glasgow, focusing on optimization and algorithmic research. He leads projects like the EPSRC-funded IP-MATCH initiative, addressing resource allocation challenges such as kidney exchange and student-doctor hospital placement systems. His work bridges theoretical contributions (e.g., integer programming, graph decomposition) and practical software development, including the Regina topology tool and kep_solver for kidney exchange analysis. Prior to Glasgow, he specialized in parallel algorithms and combinatorial topology in Australia. Key Projects: IP-MATCH (EPSRC), Regina software, kidney exchange optimization Research Themes: Combinatorial optimization, parameterized complexity, high performance computing Publications span graph theory, operations research, and algorithm design, with notable contributions to kidney exchange program modeling and spatial correlation analysis. Active collaboration with Prof. David Manlove and international teams in healthcare and computational topology.

Peter Andreas KastView profile

Lecturer

• Enzymology
• Protein Engineering
• Directed Evolution
• +3 more

Prof. Peter Andreas Kast is a Lecturer in the Department of Chemistry and Applied Biosciences at ETH Zurich. His research focuses on enzymology, protein engineering, and directed evolution, particularly investigating enzymes from pathogens such as Mycobacterium tuberculosis. He holds an honorary professorship at ETH Zurich (2011) and has received ETH medals for his diploma (1986) and doctoral theses (1991). Kast earned his Diploma in Natural Sciences (Dipl. Natw. ETH) from ETH Zurich in 1986, followed by a PhD in Microbiology. After postdoctoral work at ETH Zurich and the Scripps Research Institute, he joined ETH Zurich in 1997. His lab employs directed evolution to engineer enzymes and studies inter-enzyme allostery in metabolic pathways. Key research projects include optimizing chorismate mutase activity and elucidating structural mechanisms. His work spans enzyme kinetics, X-ray crystallography, and computational modeling. Awards include ETH medals and grants from the Swiss National Science Foundation. Teaching includes courses in Biological Chemistry. Collaborations involve structural biologists like Ute Krengel and Donald Hilvert. The Kast Lab is part of the Laboratory of Organic Chemistry at ETH Zurich, addressing challenges in biocatalysis and microbial metabolism.

• Fuzzy Logic
• Swarm Intelligence
• Computational Finance
• +7 more

Dr. Hubert Zarzycki is a researcher at the Department of Computer Science and Systems Engineering within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. His work focuses on computational intelligence, fuzzy systems, and algorithm design for optimization problems. Research Interests: Dr. Zarzycki specializes in Ordered Fuzzy Numbers and their arithmetic operations Swarm Intelligence algorithms (e.g., bacterial foraging, cuckoo search, firefly) Applications in financial modeling, sensor placement, and routing optimization Integration of blockchain technology for supply chain transparency Publication Trends: His research spans multiple domains including fuzzy logic, computational finance, and industrial IoT. Recent work emphasizes directional representation in fuzzy systems, robust risk management frameworks, and modular control systems for time-sensitive applications. Contact: Available at hubert.zarzycki@pwr.edu.pl

• Plant Physiology
• Molecular Biology of Ethylene
• Hormonal Regulation in Plants
• +7 more

Maria Del Carmen Rodriguez Gacio is a Contracted Professor and Doctor in the Department of Functional Biology at the Faculty of Biology, University of Santiago de Compostela. Her academic work spans teaching and research in plant physiology, particularly focusing on the molecular biology of ethylene and hormonal regulation in plants. She teaches core courses in Plant Physiology for Pharmacy and Biology degrees, as well as Experimental Sciences for the Master’s in Teaching. PhD in Plant Physiology, University of Santiago de Compostela, 1999 Thesis: "Alterations in the oxidation of ACC during embryogenesis and seed germination of Brassica rapa subsp. pabularia" Supervised by Dr. Angel Jesús Matilla Carro Her research interests include plant physiology, ethylene signaling, seed germination, hormonal regulation, plant stress responses, and developmental biology. She is a member of the research group "Molecular biology of ethylene" and has a long publication record in plant and environmental sciences. Her recent publications (2022–2025) reflect a strong engagement in plant molecular responses to environmental stress, species delimitation in algae, and ecological data sharing. These works span disciplines such as plant science, genetics, molecular biology, ecology, and biotechnology, indicating a multidisciplinary research profile with focus on plant adaptation and environmental interactions. Maria Del Carmen Rodriguez Gacio has contributed to over 400 publications, including articles, book chapters, and conference presentations. Her work is widely disseminated through open-access repositories like PANGAEA, Zenodo, and figshare. She is actively involved in research data management and scientific collaboration. Research group: Molecular biology of ethylene Area: Plant Physiology Department: Functional Biology School: Faculty of Biology University: University of Santiago de Compostela

Katherine HollywoodView profile

Researcher

• Metabolomics
• Mass Spectrometry
• Bioanalytical Techniques
• +5 more

Katherine Hollywood serves as the Senior Technical Specialist for Mass Spectrometry & Separations at the Manchester Institute of Biotechnology and the School of Chemistry, University of Manchester. She holds overall responsibility for facilities supporting research across the Faculty of Science and Engineering (FSE), the wider university, and external collaborations. With a PhD earned in 2010 under Prof. Roy Goodacre, her career spans post-doctoral research, SYNBIOCHEM involvement, and current leadership in analytical techniques. Her educational background includes a PhD from the University of Manchester (2010) focusing on bioanalytical techniques for skin diseases with emphasis on untargeted metabolomics. Her doctoral research, The Development of Bio-Analytical Techniques for the Treatment of Psoriasis and Related Skin Disorders , established her expertise in applying advanced analytical methods to clinical problems. Dr. Hollywood's research interests center on metabolomics, mass spectrometry applications, and bioanalytical technique development. She specializes in untargeted metabolomics approaches for clinical diagnosis and biomarker discovery, with significant contributions to Parkinson's disease research, aging studies, and synthetic biology applications. Her work intersects with multiple UN Sustainable Development Goals, particularly those addressing health and wellbeing through advanced diagnostics and biotechnology. Analysis of her 15 most recent publications reveals a strong focus on translational research bridging analytical chemistry with clinical applications. Her work spans neurodegenerative disease biomarkers (particularly Parkinson's), aging research incorporating ethnic diversity considerations, microbial engineering for compound production, analytical method validation for biosurfactants, and pandemic response diagnostics. The predominant methodologies involve advanced mass spectrometry techniques, particularly gas chromatography-mass spectrometry and LC-MS/MS approaches. Her research has been recognized through significant project funding, including leadership as Principal Investigator on the High Resolution Metabolomics project (2023-2024) and the 21ALERT initiative (2022-2023), along with contributions as Co-Investigator on multiple collaborative grants addressing microbiome engineering, protein dynamics, and synthetic biology applications. Dr. Hollywood actively participates in knowledge dissemination through the British Science Week Schools' Fair and media engagement on metabolomics applications. Her work on the Parkinsons Super Smeller project demonstrates commitment to health impact through innovative diagnostic approaches, while her research on meat adulteration detection shows application of metabolomics to food safety challenges.

• NF-κB signaling
• Oncology
• Immunology
• +7 more

Professor Guido Franzoso holds the Chair of Signal Transduction and Inflammation at Imperial College London's Faculty of Medicine. He is an Honorary Consultant at Imperial College Healthcare NHS Trust and co-Lead of the Immuno-Pathology Network, collaborating with institutions like the Institute of Cancer Research and the Francis Crick Institute. His research focuses on NF-κB pathway regulation, cancer metabolism, and immunotherapy, with translational projects in clinical trials for multiple myeloma and lymphoma. He leads the Centre for Molecular Immunology and Inflammation (CMII) and has held academic roles since 1997, including professorships at the University of Chicago. Education: M.D. (Summa Cum Laude), University of Padua, 1987 Ph.D. in Microbiological Sciences, University of Padua, 1993 Key Research Themes: Therapeutic targeting of NF-κB in oncology Cancer metabolism and precision medicine Immunosuppressive tumor microenvironment Clinical development of GADD45β/MKK7 inhibitors (DTP3) Awards: 7 patents and multiple research awards for contributions to NF-κB biology and translational oncology. Current Projects: Phase I/II clinical trials for multiple myeloma Companion biomarker discovery in CRC and other cancers Anti-cancer immuno-therapy development Affiliations: CRUK Convergence Science Centre, Membrane Receptor Network, and the Centre for Drug Discovery Science (CDDS).

Benjamin C. GorskeView profile

Associate Professor

• Peptoid Chemistry
• Synthetic Biomimetics
• Organic Synthesis
• +4 more

Benjamin C. Gorske is an Associate Professor of Chemistry and Biochemistry at Bowdoin College, currently on leave for the 2025–2026 academic year. His research lab focuses on developing synthetic mimics of biomolecules to engineer molecular machines, advanced materials, and therapeutic interventions for diseases like cancer and Alzheimer's. Education: PhD, Organic Chemistry, University of Wisconsin-Madison (2007) BA, Chemistry, Lawrence University (1995) BM, Piano Performance, Lawrence University (1995) Research Interests: The Gorske Lab employs molecular synthesis, chemical analysis, and biological activity assays to engineer peptoids (synthetic peptide analogues). Key areas include controlling peptoid secondary structures, leveraging noncovalent interactions for biomimetic functions, and applying these systems to disrupt disease mechanisms. Research spans organic chemistry, chemical biology, and materials science, with emphasis on stereoselective synthesis and structure-function relationships. Publication Trends: Gorske's 10 most recent articles (2005–2017) focus on peptoid conformation, catalytic synthesis, and biomolecular interactions. Dominant themes include helical stabilization via electronic interactions, gold-catalyzed regioselective reactions, and peptidomimetic strategies against bacterial pathogens. His work consistently bridges fundamental organic chemistry with biomedical applications. Awards: Faculty of 1000 Biology Highlight (2007) for work on tunable amide isomerism Faculty of 1000 Biology Tech Advance (2006) for pentafluoroaromatic peptoid design Lab: The Gorske Lab at Bowdoin College welcomes researchers of all experience levels to study peptoid chemistry and biomimetic systems.

Neng-Fa ZhouView profile

Professor

• Programming Languages
• Constraint Programming
• Logic Programming
• +6 more

Neng-Fa Zhou is a Professor of Computer and Information Science at Brooklyn College and the Graduate Center of the City University of New York (CUNY). He holds a BS from Nanjing University (1984), and MS and PhD from Kyushu University (1988, 1991). Before joining CUNY, he served as an Associate Professor at Kyushu Institute of Technology (1991-1999) and held visiting positions at Yale, Alberta, Tokyo Tech, and Melbourne. Specializes in programming languages, constraint logic programming, and compiler design Developed Picat and B-Prolog languages with constraint-based graphics libraries Contributed to SAT encodings, multi-agent pathfinding, and declarative programming Scientific Awards: Most Practical Paper Award at PADL 2017 Award in ASP Solver Competition for BPSolver (2011)

• Foundations of Computational Mathematics
• Geometric Integration
• Lie Group Integrators
• +12 more

Hans Z. Munthe-Kaas is a Professor in the Department of Mathematics at the University of Bergen. He holds several prominent academic leadership roles, including Chair of the Abel Prize Committee, Editor-in-Chief of Foundations of Computational Mathematics , President of the Norwegian Mathematical Society, and Project Leader of the RCN-Fripro project CODYSMA and the Lie–Størmer Center. His research lies at the intersection of pure and applied mathematics and computer science, with a focus on the Foundations of Computational Mathematics . Key areas include geometric integration, Lie group integrators, Lie–Butcher theory, structure-preserving algorithms for differential equations, multivariate Chebyshev polynomials, and computational algebra. He has pioneered coordinate-free algorithm design through the SOPHUS project and developed the DiffMan MATLAB toolbox for solving differential equations on manifolds. The recent publications show a strong trend in algebraic and geometric structures in numerical analysis, particularly in Lie–Butcher series , B-series , post-Lie algebras , and aromatic trees , with applications to integration on manifolds and homogeneous spaces. These works combine deep theoretical insights with practical computational implications. His scientific honors include the Esso Prize (1999) for his PhD work on numerical linear algebra and parallel algorithms. He has also led major research initiatives and collaborative projects, securing funding from the Research Council of Norway. Munthe-Kaas has advised students at the master’s level and welcomes new thesis projects. He has been active in teaching at all levels, including international courses at AIMS South Africa, CIMPA Brazil, and Morningside Center Beijing. He is also involved in public outreach through programs like 'Mattesirkelen' for high school students. He leads the Lie–Størmer Center and has developed software such as the SOPHUS C++ library and DiffMan toolbox, reflecting his commitment to bridging theoretical mathematics with computational practice.

Daniel WoldringView profile

Assistant Professor

• Protein Engineering
• Drug Discovery
• Therapeutics Development
• +6 more

Daniel Woldring is an Assistant Professor in the Department of Chemical Engineering and Materials Science at Michigan State University's College of Engineering, affiliated with the Institute for Quantitative Health Science & Engineering. His research focuses on developing high-performance therapeutics and diagnostics through protein engineering, combining directed evolution, high-throughput experiments, structural biology, and computational methods. Education: Ph.D., Chemical Engineering, University of Minnesota (2017) B.S., Chemical Engineering, Michigan Technological University (2011) Research Interests: His lab explores protein engineering for disease detection and treatment, including biosensor development, ancestral sequence reconstruction, GPCR ligand design, and machine learning-driven library design. Recent work includes EV-based targeting systems, electrochemical biosensors, and computational drug discovery for diseases like cancer and COVID-19. Publications: Recent articles highlight advancements in monobody engineering, machine learning for drug discovery, and synthetic biology methods. Themes include AI-driven protein design, targeted drug delivery, and evolutionary informatics. Labs/Teams: Active in the Woldring Lab, focusing on precision medicine and diagnostic tools. Collaborations span academia and industry for translational biomedical research.

Irene YarovskyView profile

Professor

• Materials Modelling and Simulation
• Nanomaterials
• Biomolecular Systems
• +3 more

Irene Yarovsky is a Distinguished Professor and the leader of the Materials Modelling and Simulation research group at RMIT University's School of Engineering in Melbourne, Australia. She also holds a Visiting Professor position at Imperial College London's Department of Materials. She earned her PhD in 1995 from Monash University, focusing on theoretical modelling of protein-surface interactions. Her research spans computational materials design, with applications in industrial coatings, bio-materials, and nanomaterials. She is particularly interested in nano-bio interfaces, including biomedical devices and environmental interactions. Prof. Yarovsky has published over 200 papers and secured over $40M in research funding. She is a Fellow of the Australian Academy of Science and Technology, the Royal Society (UK), and the Royal Australian Chemical Institute. Her work emphasizes theoretical simulations of material structures and properties, with a focus on biomolecular systems, industrial materials, and bio-nanotechnology applications. Her research group develops advanced materials for drug delivery, biosensors, and contamination-resistant surfaces. Collaborations include industry partners like BlueScope and academic institutions. She supervises PhD and Master’s students in areas like protein misfolding, graphene oxide interactions, and antimicrobial surface design. Her impact includes innovations in nanomaterials for biomedical and industrial applications.