Find Academic Supervisors

Mohamed Hibat-AllahView profile

Assistant Professor

• Quantum Computing
• Natural Language Processing
• Machine Learning
• +2 more

Mohamed Hibat-Allah is an Assistant Professor in the Department of Applied Mathematics at the University of Waterloo, where he researches the intersection of natural language processing and quantum many-body physics. His work focuses on using language models to describe quantum systems and developing machine learning solutions for combinatorial optimization problems applicable to scientific and industrial challenges. His research integrates techniques from natural language processing, quantum physics, and statistical mechanics to advance computational methods for simulating quantum materials and solving complex optimization problems. This interdisciplinary approach bridges theoretical physics with cutting-edge AI methodologies. Analysis of his recent publications reveals a strong thematic focus on quantum machine learning, neural network applications in physics, and variational methods. Key trends include the development of quantum-inspired algorithms, benchmarking of quantum models against classical counterparts, and novel approaches to simulating quantum systems using recurrent neural architectures.

Khalique NewazView profile

Researcher

• Bioinformatics
• Computational Biology
• Network Science
• +3 more

Dr. Khalique Newaz is a Postdoc researcher at Cosy.Bio and a member of the Computational Systems Biology (CDL3) group at the Center for Data Science and Computing, University of Hamburg. His work focuses on developing data and network science approaches to understand biological systems, particularly the impact of alternative splicing on protein interactions. He holds a Ph.D. in Computer Science from the University of Notre Dame, USA, where he specialized in network-based computational methods for protein 3D structure analysis. Education: Ph.D. in Computer Science , University of Notre Dame, USA (Thesis: Network-based computational methods for comparing protein 3D structures) Research Interests: Dr. Newaz’s research integrates computational biology, network science, and machine learning to address complex biological questions. Key areas include: - Protein structure prediction and classification - Systems biology approaches to aging and cancer - Network analysis of genetic and epigenetic mechanisms - Development of alignment-free computational methods for biological data. Advising & Grants: No formal advisees or grants explicitly mentioned in the provided text. Labs/Teams: He collaborates closely with the Computational Systems Biology group and is affiliated with Cosy.Bio, leveraging interdisciplinary approaches to advance systems biology.

Tiago Luís Pereira GalvãoView profile

Assistant Professor

• Materials Science
• Artificial Intelligence
• Corrosion Inhibitors
• +5 more

Tiago Luís Pereira Galvão is an Assistant Professor at the Department of Materials and Ceramic Engineering, University of Aveiro, Portugal. He is affiliated with CICECO – Aveiro Institute of Materials, where he leads research in the group G6 - Virtual Materials and Artificial Intelligence, focusing on computational materials science and corrosion prevention technologies. His work integrates artificial intelligence, machine learning, and multiscale modeling to address challenges in materials design and corrosion inhibition. Research interests include virtual materials development, corrosion inhibitors, nanomaterials, and computational approaches to materials characterization. His projects emphasize data-driven solutions, such as the DATACORTECH AI platform and CORDATA web application for corrosion inhibitor selection. Key contributions span over 14 journal articles and book chapters, with a focus on layered double hydroxides, molecular dynamics simulations, and corrosion monitoring systems. His work bridges theoretical chemistry and practical applications, such as corrosion-sensing coatings and early-stage corrosion detection using nanoadditives. Awards: Young Researchers Award 2021 Labs/Teams: CICECO – Aveiro Institute of Materials (Group G6)

Yan ZhaoView profile

Associate Professor

• Anomaly Detection
• Autoencoder
• Attention (Machine Learning)
• +5 more

Yan Zhao is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, science, and systems, with a particular emphasis on anomaly detection, machine learning, and spatio-temporal data analysis. He holds a Ph.D. in Computer Science (specific education details not explicitly provided). Research Interests: Dr. Zhao's work spans anomaly detection, autoencoders, attention mechanisms, preference learning, multivariate time series, and computational efficiency. His research often integrates machine learning with real-world applications in spatial crowdsourcing, trajectory analysis, and data privacy. Publications: With 72+ publications, his recent work emphasizes spatio-temporal prediction frameworks, federated learning, and efficient time series analysis. Notable contributions include frameworks for continuous learning on streaming data and privacy-preserving clustering in spatial crowdsourcing. Grants & Supervision: He has supervised one Ph.D. student and actively contributes to research grants focusing on data engineering and smart systems. His work bridges theoretical advancements with practical applications in transportation, social networks, and IoT.

Nicholas TruexView profile

Assistant Professor

• Molecular Immunotherapy
• Immunology
• Protein Engineering
• +5 more

Nicholas Truex is an **Assistant Professor** in the **Department of Chemistry and Biochemistry** at the **University of South Carolina**, affiliated with the **McCausland College of Arts and Sciences**. His research focuses on engineering molecular immunotherapies to enhance control of immune pathways for cancer and infectious disease treatment. Education: B.S., Chemistry, Purdue University (2007–2011) Ph.D., Chemistry, University of California, Irvine (2012–2017) Research Interests: Design of protein adjuvants and immunotherapies Peptide/protein engineering for targeted drug delivery Machine learning-driven epitope design Supramolecular peptide assemblies for vaccine kinetics Awards: NIH Postdoctoral Fellowship (2020–2022) MIT Ludwig Center Postdoctoral Fellowship (2018) UC Regents’ Dissertation Fellowship (2017) Lab & Teaching: Labs located in GSRC 411, 412, and 420 Prior teaching experience at UC Irvine in organic chemistry and spectroscopy

Aniket BhattacharyaView profile

Professor

• Theoretical Condensed Matter Physics
• Soft Matter Systems
• Polymer Physics
• +5 more

Aniket Bhattacharya is a Professor in the Department of Physics at the University of Central Florida (UCF), specializing in theoretical and computational studies of soft condensed matter physics and biophysics. His work spans DNA translocation through nanopores, intrinsically disordered proteins, and confined biopolymers, with applications in biomedical materials. PhD in Theoretical Condensed Matter Physics (University of Maryland, 1992) BS – Physics Honors (Presidency College, Kolkata) MS in Physics (University College of Science, Kolkata) Research interests: Scaling theory and simulations of soft matter systems Polymer physics in crowded environments Biologically inspired physics, particularly DNA transport through nanopores Physics of intrinsically disordered proteins linked to disease Development of soft materials for biomedical applications Recent publications focus on machine learning strategies for protein characterization, nanopore DNA barcoding, and polymer dynamics in confined spaces. His work has been supported by grants from the NSF, NASA, and NIH. He holds multiple patents related to nanopore-based DNA analysis and mutation prediction in disordered proteins. United States Patent 12,049,663B2 (2024) Non-Provisional Patent Application 19/232,158 (2025) His lab collaborates on interdisciplinary projects involving computational modeling, nonequilibrium transport phenomena, and biomedical applications.

Irene AthanassakisView profile

Professor

• Immunology
• Molecular Biology
• Cell Biology
• +3 more

Professor Irene Athanassakis is a faculty member at the Biology Department of the University of Crete. She holds a PhD in Biochemistry, Molecular Biology, Cellular and Developmental Biology from the University of Alberta (1988). Her research focuses on immunology, reproductive biology, and cancer diagnosis, with particular emphasis on MHC molecule regulation, T cell activation, and nonlinear optical imaging techniques. She leads a laboratory in the Biology building (Office: Γ 125, Lab: Ε 130) and has contributed extensively to understanding immune tolerance mechanisms and developing implantable vaccine technologies. Key research areas include soluble MHC antigens' role in immune modulation, diagnostic applications of third/harmonic generation microscopy for cancer grading, and the interplay between L-carnitine and endometriosis. Her work bridges molecular immunology with biomedical engineering, addressing clinical challenges in autoimmune diseases and cancer. Recent research trends highlight her contributions to SARS-CoV-2 immunogenicity studies, implantable vaccine design using laser-structured scaffolds, and machine learning-based analysis of biological samples. These studies underscore her interdisciplinary approach to solving complex biomedical problems. No scientific awards are explicitly listed in the provided materials. She advises no listed PhD/Master’s students but collaborates widely across disciplines. Her lab's activities include developing novel diagnostic tools and immunotherapeutic strategies, reflecting her commitment to translational research.

Byron N. Van NestView profile

Assistant Professor

• Neuroethology
• Behavioural Ecology
• Insect Neuroscience
• +3 more

Byron N. Van Nest serves as an Assistant Professor in the Department of Biological Sciences within the Faculty of Science at the University of Manitoba. His laboratory, housed in the Biological Sciences Building (Office 314), conducts research at the intersection of neurobiology and animal behavior with primary focus on invertebrate models. Dr. Van Nest's research program investigates fundamental questions about how brains select appropriate behavioral responses to environmental conditions, how neural architecture produces species-specific behaviors, and how neural systems change with age and experience. Using honey bees and cockroaches as model organisms, his lab employs multidisciplinary approaches including immunohistochemistry, confocal microscopy, electrophysiology, and sophisticated behavioral assays. Key research areas include time-memory processing in foragers, neural plasticity in mushroom bodies, sensory integration of olfactory and gustatory cues, and the neurotoxic effects of pesticides on insect cognition. Analysis of his publication record (2011-2023) reveals consistent contributions to insect neuroscience, particularly in understanding the neural basis of foraging behavior. His work demonstrates how experience shapes time-memory management in honey bees, how pesticide exposure alters synaptic organization, and how sensory modalities interact during decision-making. The research integrates molecular, anatomical, and behavioral methodologies to address questions about neural plasticity in changing environments. No scientific awards were documented in the provided materials. While the Van Nest Laboratory acknowledges funding sources, specific grant details and student mentorship records are not specified in the available text. The lab maintains active research operations focusing on neuroethological questions using invertebrate models. The Van Nest Laboratory operates within the Department of Biological Sciences at the University of Manitoba, utilizing specialized equipment for neural imaging and behavioral analysis. Current research directions include investigating pesticide impacts on neural circuits, decoding time-memory mechanisms in social insects, and exploring sensory processing pathways in honey bees under controlled laboratory conditions.

Gregg BeckhamView profile

Adjunct Professor

• Lignin and waste valorization
• Plastics upcycling
• Metabolic engineering
• +4 more

Gregg Beckham is a Senior Research Fellow at the National Renewable Energy Laboratory (NREL) and Affiliate Faculty in the Department of Chemistry at Colorado State University's College of Natural Sciences. He leads an interdisciplinary team working on biomass conversion, plastics upcycling, and sustainable bioproduct development. Education: 2007: PhD in Chemical Engineering, Massachusetts Institute of Technology 2004: MS in Chemical Engineering Practice, Massachusetts Institute of Technology 2002: BS in Chemical Engineering, Oklahoma State University Research Focus: Beckham's work integrates biochemistry, metabolic engineering, and catalysis to address sustainability challenges. Key areas include lignin valorization for renewable chemicals, enzymatic plastic degradation, development of performance-advantaged bioproducts, and bioprocess optimization for circular economy applications. Publication Trends: His recent articles emphasize enzymatic plastic recycling, lignin conversion to aviation fuels, machine learning-guided enzyme discovery, and catalytic biorefinery processes. This reflects a consistent focus on interdisciplinary solutions for plastic waste and biomass valorization. Professional Roles: Group Leader at NREL (2011-2018) Senior Lecturer at MIT (2007) Fellow at Renewable and Sustainable Energy Institute (2016-present)

Ruxandra DimaView profile

Professor

• Computational Biophysics
• Biomolecular Simulations
• Machine Learning Applications
• +2 more

Dr. Ruxandra Dima is a Professor in the Department of Chemistry at University of Cincinnati's College of Arts and Sciences, with expertise in computational biophysics and biomolecular informatics. Her research group develops multiscale models for biological nanomachines and cytoskeletal components, combining molecular simulations, machine learning, and GPU computing. Research areas include: mechanical properties of microtubules, ATP-driven molecular motors, protein-protein interactions in cellular processes, and bio-inspired material design. Her work has applications in neurodegenerative diseases and cellular transport mechanisms. Honors include: NSF CAREER Award (2009) Multiple Excellence in Undergraduate Research Mentorship Awards Cincinnati Chemist of the Year (2023) Canada Research Chair Tier II equivalent recognition Current grants support computational studies of microtubule disassembly mechanisms ($787K NSF) and undergraduate research programs. She leads interdisciplinary collaborations on biological nanomachines and organizes national sessions on molecular machines.

Charles GuView profile

Associate Professor

• Genetic Epidemiology
• Statistical Genetics
• Machine Learning in Biomedical Data
• +5 more

Charles Gu is an Associate Professor of Biostatistics and Genetics at Washington University in St. Louis, affiliated with the Roy and Diana Vagelos Division of Biology & Biomedical Sciences (DBBS), the Institute for Informatics, and the Center for Biostatistics and Data Science (CBDS). His research focuses on genetic epidemiology and statistical genetics, particularly in developing computational methods for analyzing high-dimensional genetic data and studying gene-environment interactions in complex diseases like hypertension, cardiovascular disorders, and metabolic syndromes. Key affiliations include the DBBS programs in Biomedical Informatics, Computational and Systems Biology, and Human and Statistical Genetics. He mentors PhD/MSTP students and collaborates internationally on large-scale genomic studies. Recent work includes studies on clonal hematopoiesis, lipid metabolism, and blood pressure prediction using machine learning. His 164+ publications span genetic association studies, epigenetic research, and multi-omics approaches, with a focus on translating statistical methods to clinical insights. Notable projects involve genomic analyses of atrial fibrillation, coagulation factors, and the impact of lifestyle on genetic traits.

Bruno CorreiaView profile

Associate Professor

• Protein engineering
• Computational design
• Immunoengineering
• +7 more

Bruno Correia is an Associate Professor at the Laboratory of Protein Design & Immunoengineering (LPDI) within the Institute of Bioengineering at École Polytechnique Fédérale de Lausanne (EPFL) , Switzerland. His work bridges computational protein design with experimental validation, focusing on immunoengineering applications for vaccines and cancer immunotherapy. PhD in Computational Biology (Universidade Nova de Lisboa, 2010) BSc in Chemistry (Universidade de Coimbra, 2004) Correia's research spans protein engineering , synthetic biology , and computational design , with key contributions in: Developing MaSIF for protein surface interaction analysis Designing chemically-controlled protein switches for CAR T-cell therapy Creating de novo functional proteins for vaccine development His publications highlight vaccinology , structural bioinformatics , and machine learning applications in protein science. Awards include the Protein Society Young Investigator Award (2021) and ERC Starting Grant (2016) . Scientific contributions: Protein surface fingerprinting for interaction prediction Rational immunogen design for HIV and hepatitis C Deep learning frameworks for protein structure generation

• Quantum Computing
• Quantum Algorithms
• Quantum Simulation
• +3 more

Professor Jingbo Wang is a faculty member at The University of Western Australia (UWA), serving as Head of the Physics Department and Director of QUISA (Research Hub for Quantum Information, Simulation, and Algorithms). She leads research in quantum walks, quantum simulation, and quantum algorithms. Her affiliations include the Australian Institute of Physics (Chair of WA Branch) and the ARC College of Experts. She holds a PhD from the University of Adelaide. Education: PhD in Physics and Mathematical Physics from the University of Adelaide. Previous affiliations include Murdoch University and UWA. Research focuses on quantum walks, quantum algorithms for optimization, machine learning, and network analysis. Key contributions include quantum compilers, efficient quantum circuits, and applications in complex systems. Her work bridges theory and experimental implementation, with collaborations in photonic quantum processors and quantum machine learning. Recent articles explore quantum computing applications in finance, biology, and physics. Awards include the Vice-Chancellor’s Research Mentorship Award (2022) and Australian Institute of Physics Fellowship (2021). Advising and grants involve leadership in quantum computing initiatives, including the UWA-Pawsey Educational Quantum Computing Centre. Her team develops algorithms for real-world problems like portfolio optimization and metabolic pathway analysis. She co-authored books on computational quantum mechanics and quantum walks. Labs/teams: QUISA Research Hub, UPQCC, and collaborations with institutions like CSIRO and Pawsey Supercomputing Centre.

Valeria MilamView profile

Associate Professor

• Biomolecular-Solids
• Nanostructures
• Polymers
• +3 more

Dr. Valeria Milam is an Associate Professor and MSE Faculty Fellow at Georgia Tech's School of Materials Science and Engineering, holding affiliations with AAAS and ACS. Her research integrates biomolecular solids, nanostructures, polymers, and colloidal assembly with focus areas in health, energy, and environment. She directs research on aptamers and oligonucleotide-mediated colloidal systems for diagnostic and therapeutic applications. Milam earned her B.S. from the University of Florida and M.S./Ph.D. from University of Illinois Urbana-Champaign, with postdoctoral work at University of Pennsylvania. Current projects explore DNA-directed material assembly, nanoparticle engineering, and functional oligonucleotides. She mentors graduate students including Steven Ochoa and Jiemin Fu.

• Computer-aided drug design (CADD)
• Molecular dynamics simulations
• Structural biology
• +3 more

Associate Professor Jacob D. Durrant leads the Durrant Lab at the University of Pittsburgh’s Department of Biological Sciences, affiliated with the Dietrich School of Arts and Sciences. His research focuses on computational biology, particularly computer-aided drug design (CADD), molecular dynamics simulations, and structural biology. The lab develops novel algorithms and software tools like AutoGrow, NNScore, and MolModa, emphasizing open-source platforms for drug discovery. Key projects include identifying small-molecule ligands for cancer, neurological, and infectious disease targets, as well as advancing molecular visualization through Blender and virtual reality (ProteinVR). Education includes a PhD in Chemistry, though specific details are not provided. Current graduate students include May Ahmed (machine learning in drug discovery), Nacef Guessaymi (amyloid fibril dynamics), and Joshua Mercado (LARP1 structural biology). Undergraduates and postdocs contribute to projects ranging from virtual screening to GPU-accelerated simulations. Research emphasizes ligand identification via ensemble docking, molecular visualization techniques, and the application of AI/machine learning to drug design. Recent work includes studies on SARS-CoV-2 treatments (RECOVERY trial), TEM-1 β-lactamase inhibition, and allosteric drug targets for hexokinase 2. Lab tools like FPocketWeb and PCAViz enable collaborative, browser-based molecular analysis. Advisees and collaborations extend across computational biology programs at Pitt and CMU. The lab prioritizes translational research, bridging in silico simulations with experimental validation for clinical applications. Future directions include AI-driven ligand optimization, VR-enhanced molecular exploration, and open-source software development for global research accessibility.