Find Academic Supervisors

Caroline LemieuxView profile

Assistant Professor

• Programming Languages
• Software Engineering
• Fuzz Testing
• +6 more

Caroline Lemieux is an Assistant Professor at the Department of Computer Science, University of British Columbia (UBC), with research focused on advancing software correctness, security, and performance through innovative testing and synthesis techniques. Her work bridges Programming Languages and Software Engineering , particularly in fuzz testing, specification mining, and program synthesis. PhD from University of California, Berkeley (2021), advised by Koushik Sen Postdoctoral researcher at Microsoft Research, NYC (2021-2022) Key contributions: FuzzFactory , CodaMOSA , Arvada , and Gauss Her research integrates machine learning with traditional testing methods, exemplified by projects like RLCheck (reinforcement learning for test generation) and AutoPandas (neural synthesis for dataframes). Recent publications analyze generator-based fuzzing challenges and propose hybrid strategies combining coverage feedback with AI-driven insights. Scientific Awards : ACM/SIGSOFT Best Paper Award (ESEC/FSE 2019) ACM/SIGSOFT Tool Demonstration Award (ISSTA 2019) ACM/SIGSOFT Distinguished Artifact Award (ISSTA 2019) NSERC Postgraduate Scholarship-Doctoral (PGS D) UBC Governor General's Silver Medal (2016) Teaching roles include: 2025W2: CPSC 539L - Topics in Programming Languages 2024W2: CPSC 410 - Advanced Software Engineering 2023W2: CPSC 410 (with Alex Summers) She supervises graduate and undergraduate researchers working on projects like ExploTest (automated unit test generation) and GRIMOIRE (grammar extraction from pseudo-rules). Her research team collaborates with institutions including Microsoft Research, Google, and academic partners in systems security and AI-driven testing.

Leslie Ann GoldbergView profile

Professor

• Algorithms
• Complexity Theory
• Randomised Algorithms
• +5 more

Leslie Ann Goldberg is a Senior Research Fellow at St Edmund Hall and Professor of Computer Science at the University of Oxford. She currently serves as Head of the Department of Computer Science (on sabbatical 2025-26) and focuses on foundational problems in Algorithms and Complexity Theory , particularly randomised algorithms for network communication, machine learning, and statistical physics models. Her research includes solving Aldous' 1987 conjecture on backoff protocol instability (with John Lapinskas), developing rigorous mathematical analysis frameworks for algorithmic efficiency, and advancing approximate counting techniques via Markov Chain Monte Carlo methods (with Andreas Galanis and collaborators). Key projects involve graph homomorphisms , Moran process dynamics , and #BIS complexity class analysis. Recent publications (2023-2024) span topics like Sybil defense mechanisms, low-temperature sampling on random graphs, and parameterised subgraph counting modulo 2. Her work demonstrates cross-disciplinary impact in computational biology, statistical physics, and database theory. Scientific Awards include Best Paper Prizes at ICALP 2016, ICALP 2010, and IPEC 2017. She supervises PhD student Paulina Smolarova and collaborates extensively with researchers in Oxford and beyond.

Masato KatoView profile

Professor

• Protein Phase Separation
• Low-Complexity Domains
• RNA Granule Biogenesis
• +3 more

Masato Kato serves as Professor in the Department of Biochemistry at the University of Texas Southwestern Medical Center since 2020 and concurrently as Team Leader at Japan's National Institutes for Quantum and Radiological Science and Technology. His academic trajectory includes progressive appointments from Assistant Professor (2004-2014) to Associate Professor (2014-2020) at UT Southwestern, with prior postdoctoral training at Harvard Medical School and Nara Institute of Science and Technology. 2020-present: Professor, Department of Biochemistry, UT Southwestern 2020-present: Team Leader, National Institutes for Quantum and Radiological Science and Technology, Japan 2014-2020: Associate Professor, Department of Biochemistry, UT Southwestern 2010-2014: Assistant Professor, Department of Biochemistry and Internal Medicine, UT Southwestern 2004-2010: Assistant Professor, Department of Internal Medicine, UT Southwestern 1999-2004: Postdoctoral Fellow, Ellenberger Lab, Harvard Medical School 1998-1999: Postdoctoral Fellow, Hakoshima Lab, Nara Institute of Science and Technology Dr. Kato's research pioneers the biophysical characterization of protein phase separation, particularly focusing on low-complexity domains (LCDs) in neurodegenerative disease contexts. His work establishes fundamental mechanisms of biomolecular condensate formation, including hydrogel polymerization, liquid-solid transitions, and mutation-induced dysregulation in ALS/FTD. Key contributions demonstrate how C9orf72-encoded poly-dipeptides disrupt nucleocytoplasmic transport and how redox states regulate Ataxin-2 phase behavior, bridging structural biochemistry with pathological mechanisms. Analysis of his 22 publications reveals a cohesive research program centered on LCD-driven phase transitions. The most recent 15 articles (2012-2019) systematically investigate pathological aggregation in neurodegeneration, structural basis of condensate formation, and regulatory mechanisms like phosphorylation and oxidation. This body of work establishes LCDs as central players in both physiological RNA granule assembly and disease-associated solidification, with strong emphasis on C9orf72-related ALS/FTD mechanisms. Dr. Kato maintains active leadership within the McKnight Laboratory at UT Southwestern, where his team employs integrated approaches spanning structural biology, cell biology, and biophysics to dissect phase separation mechanisms. His collaborative network includes prominent neuroscience and biochemistry groups, with co-authorship on key studies in Cell, Science, and PNAS.

Lawrence GoodridgeView profile

Professor

• Food Safety
• Antibiotic Resistance
• Foodborne Pathogens
• +6 more

Lawrence Goodridge is a Professor and Director of the Canadian Research Institute for Food Safety (CRIFS) at the University of Guelph's Ontario Agricultural College. He holds the Leung Family Professorship in Food Safety and leads research at the intersection of food safety, antibiotic resistance, and One Health principles. His work focuses on applying genomic technologies to study foodborne pathogens (E. coli, Salmonella, Listeria, Cronobacter) and leveraging wastewater surveillance for infectious disease outbreak prediction. Academic History: BSc Microbiology (University of Guelph, 1995), MSc Food Microbiology (2003), PhD Food Microbiology (2002), followed by post-doctoral training in Food Safety at the University of Georgia (2002). Joined CRIFS in 2003. Research Interests: Genomic analysis of pathogen emergence, wastewater-based epidemiology, bacteriophage applications, and consumer education strategies for food safety. His lab develops innovative methods for rapid pathogen detection in food systems and environmental samples. Articles Trends: Over 100 peer-reviewed publications emphasize genomic surveillance of foodborne pathogens and SARS-CoV-2, with a focus on wastewater sampling innovations. Recent work explores multi-modal data integration for public health forecasting and ethical data protection frameworks for surveillance programs. Awards: While no specific prizes are listed, his $50M+ research funding from Canadian/international sources underscores recognition of his impactful work. Grants support projects like phage-based sanitization and antimicrobial resistance tracking. Advising & Labs: Leads CRIFS laboratory operations and collaborates globally on food safety initiatives. His research has informed food industry guidelines and policy frameworks for mitigating pathogen risks in agricultural and environmental systems.

• Generative AI
• Information Theory
• Machine Learning
• +4 more

Alexandros G. Dimakis is a Professor at the University of California, Berkeley's Department of Electrical Engineering and Computer Sciences (EECS), College of Engineering. He is also Co-Director of the National AI Institute for Foundations of Machine Learning and Co-Founder of BespokeLabs.ai. PhD (2008) and Diploma (2003) in Electrical Engineering His research focuses on Generative AI , Information Theory , and Machine Learning . Recent work includes advancements in diffusion models, compressed sensing, and causal inference. His publications (150+) emphasize inverse problems, neural network verification, and generative model optimization. Recent publications highlight trends in Diffusion Models for inverse problems, Language Model Scaling , and 3D-Aware Generative Systems . Collaborative projects span biomedical applications, large-scale dataset curation (Datacomp-LM), and parameter-efficient model fine-tuning. Scientific Awards : IEEE Fellow (2022) James Massey Award (2018) NSF CAREER Award (2011) Google Research Faculty Award Best Paper awards at UAI workshops Eli Jury Dissertation Award (UC Berkeley) He advises PhD students in generative modeling, compressed sensing, and information theory. His research group collaborates with institutions like MIT, NYU, and IBM Research. Former students hold positions at Google, Amazon, and academic institutions like Purdue University.

Thomas LamView profile

Professor

• Algebraic Combinatorics
• Total Positivity
• Cluster Algebras
• +5 more

Thomas Lam is a professor of mathematics at the University of Michigan , specializing in algebraic combinatorics, total positivity, and connections to mathematical physics. His work bridges cluster algebras, positive geometry, and integrable systems, with applications to scattering amplitudes in quantum field theory. Lam has collaborated extensively with physicists such as Nima Arkani-Hamed and mathematicians like Pavlo Pylyavskyy and Mark Shimozono. Key research areas: Cluster algebras, total positivity, electrical networks, positroid varieties, and quantum cohomology. Notable contributions: Defining polypositroids, proving regularity theorems for totally nonnegative flag varieties, and establishing cluster structures in braid varieties. Recent work focuses on positive geometries , including the amplituhedron and moduli spaces of points on projective lines, with implications for particle physics. His articles often explore dual graded graphs, K-theoretic Schubert calculus, and the interplay between combinatorics and algebraic structures. Lam's research has been supported by NSF grants, including DMS-0748636 and DMS-1249708 .

Mona SinghView profile

Professor

• Computational Biology
• Machine Learning
• Algorithms
• +3 more

Mona Singh is a Professor of Computer Science at Princeton University, with affiliations to the Lewis-Sigler Institute for Integrative Genomics and the Department of Molecular Biology. She has been a faculty member since 1999. Ph.D., Massachusetts Institute of Technology, 1995 A.B. and S.M. degrees in Computer Science from Harvard University Her research focuses on computational molecular biology, integrating machine learning and algorithms to analyze biological networks, protein interactions, and mutational impacts. Key areas include DNA/RNA binding prediction, protein structure analysis, and network-based disease gene discovery. Her recent work highlights trends in protein language models, kinase-substrate prediction, and equitable MHC binding algorithms. These span sub-fields like structural bioinformatics, network biology, and functional genomics. Scientific Awards: Presidential Early Career Award for Scientists and Engineers (PECASE) Rheinstein Junior Faculty Award ACM Fellow (2019) ISCB Fellow (2018) She has taught an introductory computational biology course with Professor Coleen Murphy, covering sequence analysis, phylogenetics, and network reconstruction. Her group has developed tools like dPUC , nCOP , and DiffMut . Her lab collaborates with institutions including Carnegie Mellon, Duke University, and the Broad Institute, advancing applications in cancer genomics, metabolic disease, and precision medicine.

• Evolutionary Computation
• Machine Learning
• Natural Computing
• +5 more

Thomas Bäck is a Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University , Netherlands, and a member of the interdisciplinary programme Society, Artificial Intelligence and Life Sciences (SAILS) . His academic career spans roles at Leiden University (1996–present) and leadership positions at the Center for Applied Systems Analysis in Dortmund (1994–2000). Education : Diplom-Informatiker (Computer Science), Technische Universität Dortmund (1990) Dr. rer. nat. (Computer Science), Technische Universität Dortmund (1994) Research Interests : Dr. Bäck specializes in evolutionary computation , machine learning , and their applications in sustainable smart industry and healthcare . Recent work focuses on integrating large language models (LLMs) and quantum computing into optimization frameworks, with projects like CIMPLO (predictive maintenance), ECOLE (experience-based optimization), and SAPPAO (airline operations optimization). Scientific Contributions : His 526+ publications cover evolutionary algorithms, quantum optimization, and LLM-driven design, with recent trends including: Quantum computing (e.g., quantum approximate optimization, quantum advantage challenges) LLM integration (e.g., hyperparameter tuning, mutation control, code evolution graphs) Healthcare and industry (e.g., predictive maintenance, anomaly detection, melt quality prediction) Algorithm benchmarking (e.g., IOHprofiler, MA-BBOB, explainable benchmarking) Scientific Awards : IEEE Fellow (2022) Royal Netherlands Academy of Arts and Sciences (KNAW) member (2021) Academia Europaea member (2022) IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2015) Fellow, International Society of Genetic and Evolutionary Computation (2003) Best Ph.D. thesis award, German Society of Computer Science (GI) (1995) Advising and Grants : He supervises Ph.D. candidates in evolutionary computation and machine learning and has secured 7 major grants from organizations like the Dutch Research Council , European Commission , and The Research Council of Norway . His editorial roles include Editor-in-Chief of the Evolutionary Computation Journal and associate editorships in leading AI journals.

Brian HieView profile

Assistant Professor

• Machine Learning
• Computational Biology
• Protein Engineering
• +6 more

Brian Hie is an Assistant Professor of Chemical Engineering at Stanford University , a Dieter Schwarz Foundation Stanford Data Science Faculty Fellow , and an Innovation Investigator at Arc Institute . He leads the Laboratory of Evolutionary Design , focusing on the intersection of biology and machine learning . His prior roles include a Stanford Science Fellow in the Stanford University School of Medicine and a Visiting Researcher at Meta AI . Education: Ph.D. , Electrical Engineering and Computer Science , Massachusetts Institute of Technology (2021) Bachelor’s Degree , Stanford University Research Interests: Brian’s work bridges machine learning and computational biology , with a focus on protein engineering , single-cell RNA sequencing , and viral evolution . His Evolutionary velocity framework predicts protein evolutionary dynamics across timescales, while his Scanorama algorithm enables efficient integration of heterogeneous single-cell datasets. He also develops structure-informed language models for antibody optimization and uncertainty-aware ML for biological discovery. Publication Trends: His recent work (2023) emphasizes structure-based inverse folding for antibody evolution, evolutionary scale modeling , and unsupervised optimization . Earlier studies (2022-2021) cover evolutionary velocity , multi-modal single-cell analysis , and viral escape prediction using natural language analogies. Scientific Awards: Stanford Science Fellow (2021) National Defense Science and Engineering Graduate Fellowship (2019) Advising: He mentors doctoral students including Brandon Ameglio , Garyk Brixi , and Chang M. Yun , with a focus on biological design and computational methods . Labs & Collaborations: His lab collaborates with Bio-X and the Institute for Human-Centered Artificial Intelligence (HAI) , and he maintains affiliations with Sarafan ChEM-H and Stanford Data Science .

Michael BaymView profile

Associate Professor

• Microbial Evolutionary Genomics
• Antibiotic Resistance
• Computational Biology
• +7 more

Dr. Michael Baym is an Associate Professor of Biomedical Informatics at Harvard Medical School with affiliate appointments in Microbiology and the Laboratory of Systems Pharmacology, and as an Associate Member of the Broad Institute. He leads the Baym Lab, which studies microbial evolutionary genomics and antibiotic resistance through a hybrid of experimental, computational, and theoretical approaches. His research focuses on: Antibiotic Resistance Evolution and practical interventions Mobile Genetic Elements (plasmids, phages, transposons) Computational Genomic Algorithms for big data analysis Synthetic Biology tools and technologies Key recent publications explore phage discovery systems , phylogenetic compression of microbial genomes, and RNA-guided gene drives in plasmids. His work is supported by multiple NIH/NIGMS and NSF grants including a MIRA award. Scientific honors include: Packard Fellowship (2018) Pew Biomedical Scholarship (2020) Sloan Research Fellowship (2020) A. Clifford Barger Excellence in Mentoring Award (2021) SSQBio Mentorship Award (2022) The lab actively trains PhD students and postdoctoral fellows with alumni occupying academic and industry positions globally. Current team members include researchers from interdisciplinary backgrounds working at the intersection of experiment, computation, and theory .

Ana DamjanovicView profile

Research Professor

• Ion Channels
• Protein Electrostatics
• Membrane Electrostatics
• +3 more

Ana Damjanovic is an Assistant Research Professor in the Thomas C. Jenkins Department of Biophysics at Johns Hopkins University (JHU), affiliated with the Zanvyl Krieger School of Arts & Sciences. Her research focuses on ion channels, protein and membrane electrostatics, and computational biophysics. She holds a Ph.D. in Physics from the University of Illinois at Urbana-Champaign, where she studied quantum physics of photosynthetic light harvesting under Prof. Klaus Schulten. Subsequent postdoctoral research included work on photosynthesis with Prof. Graham Fleming at UC Berkeley, and molecular dynamics studies of protein ionization at JHU. Her current lab investigates ion channel mechanisms, protonation dynamics, and electrostatic effects in biological systems using advanced computational tools. Group members include graduate student Nauman Sultan (co-supervised with NIH's Bernard Brooks) and undergraduates Marianne Ri and Vivek Booshan. Past advisees include Ada Chen (now a NIH postdoc) and Maggie Li. Key research contributions include developing pH replica exchange methods, protein pKa prediction using machine learning, and structural-functional studies of voltage-gated sodium channels. Her work has been published in high-impact journals like Proceedings of the National Academy of Sciences and Biophysical Journal . Lab affiliations include the Computational Biophysics Group at JHU, with access to cutting-edge simulation techniques and experimental validation platforms. Ongoing projects explore ion channel selectivity, membrane protein dynamics, and computational modeling of protonation-dependent phenomena.

Colin SempleView profile

Professor

• Cancer genomics
• Structural mutations
• Molecular evolution
• +5 more

Professor Colin Semple is a leading researcher at the University of Edinburgh's Institute of Genetics and Cancer (IGC), where he serves as Group Leader and Head of Bioinformatics. His work is conducted within the MRC Human Genetics Unit, focusing on computational genomics and the analysis of structural mutations in both germline and cancer contexts. Professor Semple's research investigates the origins and impacts of structural mutations in the human genome, with particular emphasis on how these alterations affect gene function in developmental contexts and drive cancer progression. His group studies complex structural rearrangements in challenging cancer types including ovarian cancer, glioblastoma, and mesothelioma, where tumor genomes undergo dramatic reorganization. The research is guided by four key questions: What are the origins of structural mutations? How do they impact gene function? How does structural complexity drive disease progression? How do diverse mutational constellations combine to create adaptations and vulnerabilities? Analysis of Professor Semple's publications reveals a consistent focus on structural variation in cancer genomics, with particular attention to ovarian cancer mechanisms, lesion segregation in tumor evolution, and the functional consequences of genomic rearrangements. His work frequently employs whole genome sequencing approaches to uncover previously hidden layers of genomic variation that affect more of the genome than traditional short variants. Professor Semple leads a substantial research team including bioinformaticians and PhD students, and oversees the Bioinformatics Analysis Core which provides collaborative expertise to over 500 researchers at the IGC. His group maintains strong collaborations with both local researchers at the University of Edinburgh and international consortia, working closely with clinicians to translate genomic findings into potential diagnostic and therapeutic approaches. The Semple Lab is funded by major organizations including the Medical Research Council (MRC), Cancer Research UK (CRUK), and the Chief Scientist Office (CSO).

Celeste SaguiView profile

Professor

• Computational Biophysics
• Biomolecular Simulations
• Free Energy Methods
• +5 more

Celeste Sagui is a Professor in the Department of Physics at North Carolina State University (NC State), affiliated with the College of Sciences. She holds additional roles as a faculty affiliate in Genomics Sciences at NC State and is a member of the Center for High Performance Simulation. Her research focuses on computational biophysics, biomolecular simulations, and free energy methods applied to nucleic acid structures, protein dynamics, and nanotechnology systems. She has contributed to the AMBER simulation package development, co-authoring versions from 10 to 14. Education: Doctorate in Physics, University of Toronto (1995) Licentiate degree, National University of San Luis, Argentina Research Interests: Sagui’s work explores DNA/RNA structure and phase transitions, electrostatic interactions, and methodologies for large-scale molecular simulations. Recent studies include nucleic acid hairpin instabilities linked to neurodegenerative diseases, polyglutamine aggregation mechanisms, and novel DNA motifs like the eGZ structure in Z-DNA. She employs quantum chemistry, density functional theory, and phase-field models to investigate systems ranging from biomolecules to nanomaterials. Publications: Her recent work emphasizes nucleic acid dynamics, free energy landscapes, and computational methods for studying diseases such as Friedreich’s ataxia and polyglutamine disorders. Key contributions include advancements in laser-driven simulations and infrared spectroscopy analysis of protein structures. Labs/Teams: Active in the Center for High Performance Simulation, focusing on high-throughput computational modeling and collaborative software development for biomolecular research.

Peter A. JonesView profile

Professor

• Epigenetics
• DNA methylation
• Cancer epigenetics
• +7 more

Peter A. Jones is President and Chief Scientific Officer at the Van Andel Institute (VAI) in Grand Rapids, Michigan, where he leads the Department of Epigenetics. He previously served as Director of the USC Norris Comprehensive Cancer Center from 1993 to 2011 and has been a central figure in advancing epigenetics research, particularly in cancer. His laboratory investigates DNA methylation, chromatin dynamics, and epigenetic therapies. Research Interests: Dr. Jones's work centers on epigenetic mechanisms in cancer, including DNA methylation, histone modifications, nucleosome positioning, and the therapeutic potential of epigenetic drugs. His research has pioneered the use of DNA methylation inhibitors like 5-azacytidine and explored viral mimicry as a mechanism for immune activation in cancer. He also studies transposable elements and their role in gene regulation and immune response. Publication Trends: His recent publications (2021–2024) reveal a strong focus on the interplay between epigenetics and immunotherapy, particularly how DNA methyltransferase inhibitors (DNMTi) induce viral mimicry, enhance immune recognition, and improve responses to checkpoint blockade. Studies span hematological malignancies, solid tumors, and T cell biology, with frequent collaboration with Stephen Baylin and others. Scientific Awards: Member, National Academy of Sciences Member, National Academy of Medicine Fellow, AACR Academy Fellow, AAAS Fellow, American Academy of Arts and Sciences Kirk A. Landon Award for Basic Cancer Research (2009) Medal of Honor, American Cancer Society (2011) Outstanding Investigator Grant, NCI Harvey Prize (2024) Advising and Grants: Dr. Jones mentors multiple postdoctoral fellows, graduate students, and research scientists. His lab is supported by major grants, including the VAI-SU2C Epigenetics Dream Team, which has launched 15 clinical trials. He has received sustained funding from the National Cancer Institute and collaborates with institutions worldwide to advance epigenetic therapies. Labs and Teams: He leads the Peter Jones Laboratory at VAI, a multidisciplinary team investigating epigenetic regulation in cancer. The lab includes computational biologists, clinical researchers, and molecular biologists, working on both basic mechanisms and translational applications. The team is part of larger collaborative initiatives such as the VAI-SU2C Epigenetics Dream Team and the International Linked Clinical Trials Program.

Mykel Loren BrinkerhoffView profile

Researcher

• Phonology
• Phonetics
• Linguistic Fieldwork
• +3 more

Mykel Loren Brinkerhoff is a Postdoctoral Research Associate and Acting Phonetics Lab Director in the Department of Linguistics at the University of Washington. His research focuses on phonology, phonetics, and linguistic fieldwork, particularly on Mesoamerican languages like Santiago Laxopa Zapotec. He investigates syntactic and prosodic phenomena in Celtic and Germanic languages and collaborates on projects such as the Zapotec Language Project. Brinkerhoff has taught at the University of California, Santa Cruz, and the University of North Carolina at Chapel Hill, leading courses in phonetics, phonology, and syntax. He mentors students in fieldwork and linguistic analysis, with notable projects on Zapotec voice quality and Scandinavian syntax. His work bridges theoretical linguistics with empirical research, contributing to our understanding of phonological interfaces and language structure. His publications explore topics like voice quality in Zapotec, Norwegian object shift, and Welsh morphophonology. He actively contributes to academic discourse through presentations at conferences like LabPhon and SSILA. Brinkerhoff’s research also emphasizes documenting endangered languages and advancing methodologies in acoustic phonetics.