جست‌وجوی استاد و پژوهشگر

Jennifer Rossمشاهده پروفایل

استاد

• Biophysics
• Soft Matter Physics
• Biological Condensed Matter Physics
• +۵ مورد دیگر

Jennifer Ross is a Professor of Physics and Associate Dean for Creativity, Scholarship, and Research at the College of Arts & Sciences of Syracuse University . As a biophysicist, she investigates how cells organize their interiors through self-assembly and active matter principles, focusing on the microtubule cytoskeleton and enzyme-driven systems using single-molecule imaging . Her research bridges fundamental physics with biological organization . Education: Ph.D. in Physics, University of California, Santa Barbara (2004) B.A. in Physics and Mathematics, Wellesley College (2000) Research Focus: Self-organization of cytoskeletal networks Active matter dynamics in biological systems Motor protein interactions and cargo transport Programming circadian materials via biomolecular systems Microtubule severing mechanisms Recent Article Trends: 2025 studies explore kinesin-driven cytoskeletal composites, urease-DNA origami engineering, and crosslinker-regulated network mechanics 2024-2023 work examines ionic strength effects on microtubules, programmable circadian materials, and motor-cargo dynamics Earlier studies analyze actin-microtubule composites, liquid crystal phase control, and severing enzyme mechanisms Scientific Awards: Fellow of the American Physical Society (APS) and American Association for the Advancement of Science (AAAS) Cottrell Scholar (2025) and STAR Award Margaret Oakley Dayhoff Award (Biophysical Society) Grants: Leads multiple NSF, Sloan Foundation, and Research Corporation grants for projects like "Energy and Entropy Sculpting" and "Explorations: SUPER-Tech SHIP" . Teaching: Offers courses in experimental physics, microscopy, and biophysics, including a globally adopted hands-on microscope-building curriculum. Lab: Heads the Bio-Active Matter Lab , studying how cells harness noisy systems for autonomous organization.

Omar A. Salehمشاهده پروفایل

استاد

• Biophysics
• Soft Matter Physics
• Polymer Physics
• +۵ مورد دیگر

Professor Omar A. Saleh is a distinguished physicist and materials scientist at the University of California, Santa Barbara, holding appointments in both the Materials and Physics Departments. Since summer 2023, he has served as Chair of the Materials Department and maintains a minority appointment in the Biomolecular Science and Engineering (BMSE) Program, where he previously served as Director from 2013-2017. His educational background includes a B.S. in Physics from MIT (1997) and a Ph.D. in Physics from Princeton (2003), supported by a Hertz Fellowship. Following postdoctoral work at École Normale Supérieure in Paris developing single-molecule techniques for motor protein/DNA studies, he joined UCSB in 2005. Saleh's research centers on fundamental principles of biomolecular behavior through experimental investigation of biopolymer elasticity and biomimetic organelles. His lab pioneers precision single-molecule stretching experiments to study entropic/energetic contributions in soft systems and creates life-like behaviors using reconstituted nucleic acid/protein assemblies. Key focus areas include DNA nanostar phase separation, liquid-liquid phase behavior, intrinsically disordered proteins, and non-equilibrium biomolecular systems. His publication trends reveal a strong emphasis on biomolecular condensates (2023-2025), with recurring themes in DNA nanotechnology, polyelectrolyte physics, and single-molecule mechanics. Recent work explores tension-mediated control of phase separation, transcriptional regulation of biomolecular liquids, and active matter principles in DNA systems. NSF CAREER Award (2008) Bessel Research Award from Alexander von Humboldt Society (2017) Fellow of the American Physical Society (2019) Saleh actively mentors graduate students and postdocs including Sam Wilken, Gabrielle Abraham, Anna Nguyen, and Aria Chaderjian, whose research spans DNA nanostar liquids, active droplets, and complex coacervation. His lab develops innovative instrumentation including high-speed magnetic tweezers and GPU-based tracking systems, supported by grants such as NSF/MCB-BSF: Direct force measurements of intrinsically disordered proteins. The Saleh Group operates at BioE 3006, focusing on creating quantitative models of biological function through physical reconstitution.

• Machine Learning
• Explainable AI
• Neural Networks
• +۳ مورد دیگر

Jonas Fischer is the head of the Explainable Machine Learning group at the Max Planck Institute for Informatics, Department of Computer Vision and Machine Learning. His research focuses on interpreting complex machine learning models, particularly in genomics and healthcare, aiming to enhance robustness and alignment with human decision-making. Prior to his role at MPI, he was a postdoctoral fellow at Harvard University's Department of Biostatistics, where he worked on interpretable models for gene regulatory systems in cancer. Education: PhD in Computer Science from Saarland University (2022), with a thesis titled More than the sum of its parts , exploring the intersection of pattern mining and deep learning. He has contributed to advancing methods in neural network pruning, federated learning, and low-dimensional embeddings (e.g., dtSNE, Mercat). His work bridges computational biology, data mining, and machine learning, with applications in DNA methylation analysis, graph-based differential networks, and biomedical informatics. Key research areas include: (1) Explainable AI and neural network interpretability, (2) Biomedical applications of machine learning (e.g., gene regulatory networks, cancer genomics), (3) Low-dimensional embeddings and visualization techniques, (4) Federated learning for privacy-preserving collaborative models, and (5) Pattern mining for error analysis in NLP and classification tasks. Publications span top venues like NeurIPS, ICLR, Bioinformatics, and Genome Biology. His group develops tools such as BONOBO for omics data integration and node2vec2rank for scalable graph analysis. He actively collaborates with biomedical researchers to address challenges in data-driven healthcare and precision medicine.

Han Liuمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Systems
• Statistical Machine Learning
• +۴ مورد دیگر

Han Liu is a Professor in the Department of Computer Science at Northwestern University's McCormick School of Engineering. He directs the MAGICS (Modern Artificial General Intelligible and Computer Systems) Lab and the Center for Foundation Models and Generative AI at Northwestern, with prior roles as director of the Deep Reinforcement Learning Center at Tencent AI Lab and professor at Princeton and Johns Hopkins Universities. PhD in Machine Learning and Statistics from Carnegie Mellon University (2012), advised by John Lafferty and Larry Wasserman Han Liu's research focuses on integrating artificial intelligence with computer systems, particularly through foundation models and probabilistic graphical models. His work aims to revolutionize science, engineering, and business by deploying statistical machine learning methods in edge and cloud computing environments. Recent research trends include transformer-based models, modern Hopfield networks, genomic foundation models, and theoretical analysis of attention mechanisms. His 2025 publications explore topics like species differentiation with DNA embeddings, universal approximation capabilities of transformers, and metaverse spatial reasoning. Alfred P Sloan Fellowship in Mathematics IMS Tweedie New Researcher Award ASA Noether Young Scholar Award NSF CAREER Award Presidential Early Career Awards for Scientists and Engineers Han Liu serves as associate editor for the Journal of American Statistical Association, Electronic Journal of Statistics, Technometrics, and the Journal of Portfolio Management. He has directed research centers at Northwestern and contributed to major conferences as area chair (NeurIPS, ICML, ICLR).

Alexis Battleمشاهده پروفایل

دانشیار

• Genomics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Alexis Battle is an Associate Professor at Johns Hopkins University with appointments in Biomedical Engineering , Computer Science , and Genetic Medicine (secondary). She directs the Malone Center for Engineering in Healthcare and serves as Deputy Director of the Data Science and AI Institute . Educated at Stanford University (PhD in Computer Science, 2013), Battle transitioned to academia after leadership roles at Google. Research Focus: Battle’s work bridges genomics and machine learning , emphasizing the impact of genetic variation on human health. Her lab develops tools like Watershed to predict functional effects of rare variants, aiming to enhance rare disease diagnosis. Key themes include non-coding DNA analysis , personalized genomics , and systems biology , with applications in cardiovascular disease and neurodegenerative disorders . Publications & Awards: Over 60 peer-reviewed articles in journals like Nature , Science , and Genome Biology , with recent emphasis on single-cell transcriptomics , multiomics integration , and telomere biology . Recipient of the President’s Frontier Award (2022), Microsoft Investigator Fellowship (2019), and Searle Scholar (2016). Scientific Awards: 2022 President’s Frontier Award 2019 Microsoft Investigator Fellowship 2019 Johns Hopkins Discovery Award 2017 Johns Hopkins Catalyst Award 2016 Searle Scholar Advising & Funding: Mentors 11 PhD students, 3 undergraduates, and postdoctoral fellows. Her research is funded by NIH, Searle Scholars, and institutional grants. The Battle Lab collaborates on projects like the GTEx Consortium , focusing on gene regulation and clinical genomics .

Yaojun Zhangمشاهده پروفایل

استادیار

• Biological Physics
• Biophysics
• Theoretical Physics
• +۳ مورد دیگر

Yaojun Zhang is an Assistant Professor in the Department of Physics & Astronomy and the Department of Biophysics at Johns Hopkins University. She earned her PhD in Physics from the University of California, San Diego (2015), followed by postdoctoral fellowships at the Princeton Center for Theoretical Science (2015-2018) and the Princeton Center for the Physics of Biological Function (2018-2021). Her research focuses on biological physics, particularly the complex behaviors of biomolecules and their assemblies across scales—from single-molecule folding to intracellular transport and biomolecular phase separation. She employs theoretical, mathematical, and computational tools to bridge biological questions with physical principles. Education PhD in Physics, University of California, San Diego (2015) Postdoctoral Fellowships: Princeton University (2015-2021) Research Interests Her group studies biomolecular condensates and liquid-liquid phase separation, exploring how microscopic interactions determine macroscopic properties of cellular compartments. Key areas include: Biomolecular condensate formation and dynamics Phase separation in cellular environments Interactions between biomolecules and cellular components Biophysics of intracellular transport Collaborations & Tools Zhang collaborates with experimentalists to validate theoretical models and develops frameworks for understanding condensate functions, such as surface tension, stoichiometry, and phase diagrams. Her work addresses challenges like condensate stability, molecular exclusion, and biological function regulation. Labs & Resources She leads the Zhang Lab , which integrates experimental and computational approaches. Her team’s research is supported by resources at the Bloomberg Center for Physics and Astronomy.

Volodymyr Kuleshovمشاهده پروفایل

استادیار

• Machine Learning
• Generative Models
• Probabilistic Methods
• +۴ مورد دیگر

Volodymyr Kuleshov is an Assistant Professor at Cornell Tech and Cornell University's Department of Computer Science. His research focuses on machine learning, particularly generative models, probabilistic methods, and applications in health and sustainability. He co-founded Afresh, an AI startup reducing food waste, and has commercialized genome sequencing work via Moleculo (now part of Illumina). Kuleshov earned his PhD from Stanford University, advised by prominent figures like Stefano Ermon and Serafim Batzoglou. He teaches courses like CS 5785 (Applied Machine Learning) and CS 6785 (Advanced Topics in Machine Learning). His awards include the NSF CAREER Award and Arthur Samuel Best Thesis Award. Education: PhD in Computer Science from Stanford University (2018), advised by Stefano Ermon, Serafim Batzoglou, Michael Snyder, Christopher Re, and Percy Liang. Research Interests: Core ML (generative models, approximate inference), health tech (genome sequencing, clinical decision support), sustainability (AI-driven food waste reduction). Notable projects include Caduceus for DNA sequence modeling and Diffusion Duality theory. Awards: Google Research Scholar Award (2025), Outstanding Paper Award (EMNLP 2023), NIH MIRA Award (2023). Students/Advising: Over 20 advisees across PhD, Master’s, and undergraduate programs, including Edgar Marroquin (PhD) and Charlie Marx (Stanford). Alumni include Allan Bishop (Bloomberg) and Yong Huang (UCI PhD). Labs/Teams: Leads research groups at Cornell Tech focusing on generative AI and its real-world applications. Collaborates with institutions like MILA (Montreal) and DeepMind.

Massimiliano Di Ventraمشاهده پروفایل

استاد

• Quantum transport
• Non-equilibrium statistical mechanics
• DNA sequencing
• +۶ مورد دیگر

Massimiliano Di Ventra is a Professor of Physics at the University of California, San Diego, holding this position since 2006 after serving as Associate Professor (2004-2006) and moving from Virginia Tech where he was Assistant Professor (2000-2002) and Associate Professor (2003). His academic journey includes research roles at IBM T.J. Watson Research Center and Vanderbilt University. Education: Undergraduate degree in Physics, summa cum laude, University of Trieste, Italy (1991) PhD in Physics, Swiss Federal Institute of Technology, Lausanne (1993-1997) Di Ventra's research bridges theoretical physics with practical applications, focusing on quantum transport in nanoscale systems, non-equilibrium statistical mechanics, and DNA sequencing/polymer dynamics in nanopores. His pioneering work on memory effects in nanostructures has driven innovations in unconventional computing and biophysics, resulting in over 300 refereed publications and significant commercial impact through memcomputing technologies. His scientific awards include: Senior member, National Academy of Inventors (2025) Humboldt Research Award (2024) Fellow, American Association for the Advancement of Science (2022) IEEE Nanotechnology Council Distinguished Lecturer (2022) Fellow, IEEE (2022) George W. and Carol A. Lattimer Research Fellow (2021) Member of Academia Europaea (2020) Feynman Prize for Theory in Nanotechnology (2020) APS Outstanding Referee (2019) Highly Cited Researcher (2018) Di Ventra has secured major research funding including an NSF CAREER Award and Ralph E. Powe Award, holds 11 granted patents, and co-founded MemComputing, Inc. His mentorship extends to numerous graduate students and postdoctoral researchers within his active research group at UCSD. He maintains a globally recognized research presence through visiting professorships at institutions including École Normale Supérieure of Paris (2024), Technion (2017), and Technical University of Dresden (2015), and has delivered over 350 invited talks including 20 plenary/keynote presentations worldwide.

• Statistical Methods in Biomedical Research
• Biomarker Analysis
• Clinical Trial Design
• +۷ مورد دیگر

Todd A. Alonzo is a Professor of Research in the Department of Preventive Medicine at the University of Southern California . As Group Statistician for the Children's Oncology Group , he focuses on statistical methods for biomarker analysis, medical diagnostic testing, and clinical trial design in pediatric acute myeloid leukemia (AML). Education: B.S. in Statistics, California State Polytechnic University (1994) MS and PhD in Biostatistics, University of Washington (1997, 2000) Research Interests include: Development of statistical frameworks for diagnostic accuracy Genomic and proteomic profiling in AML Pharmacogenomic score systems for chemotherapy response Non-inferiority trial design in low-event-rate settings Health disparities in pediatric oncology Scientific Awards : Fellow, American Statistical Association (2018) Outstanding Teacher Award, International Society for Magnetic Resonance in Medicine (2017) NIH Predoctoral Cardiovascular Biostatistics Training Grant (1995) ENAR Biometrics Society Distinguished Student Paper Award (1999) WNAR Biometrics Society Best Student Oral Presentation (1999) Leadership & Service includes editorial board memberships (Biometrics, Pediatric Blood & Cancer, Biometrical Journal), reviewer for 30+ scientific journals, and roles on multiple Data Safety and Monitoring Boards. He served as President of the International Biometric Society Western Northern America Region (WNAR) in 2009.

Celeste Saguiمشاهده پروفایل

استاد

• Computational Biophysics
• Biomolecular Simulations
• Free Energy Methods
• +۵ مورد دیگر

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.

Adrian Linacreمشاهده پروفایل

استاد

• Forensic Science
• Forensic DNA Analysis
• Wildlife Forensic Science
• +۱۲ مورد دیگر

Adrian Linacre is a Professor and Chair in Forensic DNA Technology at Flinders University, within the College of Science and Engineering, Department of Biological Sciences. He is a leading figure in forensic science, with a focus on DNA analysis, wildlife forensics, and crime scene investigation. BSc in Biological Sciences (Hons), University of Edinburgh, 1984 DPhil in Molecular Genetics, University of Sussex, 1988 His research centers on getting more from less at crime scenes , particularly through developing highly sensitive DNA typing methods and studying the transfer and persistence of biological materials. He also pioneers the use of non-human DNA in forensic investigations, notably in wildlife forensic science , aiding in species identification and combating illegal wildlife trade. His recent publications reflect a strong trend in trace DNA analysis , body fluid identification , and DNA transfer dynamics , with applications in drug cases, sexual assault investigations, and environmental DNA degradation. His work increasingly integrates molecular techniques with real-world forensic challenges. Notable scientific awards include: Medal of the Order of Australia (OAM), 2020 Inspirational Scientist of the Year, Royal Society of Edinburgh, 2005 Fellow of the Royal Society for the Encouragement of Arts and Commerce (FRSA) Finalist, South Australian Science Excellence and Innovation Awards (2023, 2024) He has successfully supervised several students, including Piyamas Kanokwongnuwut and Alicia Haines, many of whom have won international recognition. He has secured significant research funding and contributed to national and international forensic policy, including a key review for the UK Home Office on low-template DNA. His professional leadership includes presidencies of the ANZFSS and ISFG, and vice presidency of the IAFS. Linacre is actively involved in editorial roles, serving as Associate Editor for Forensic Science International: Genetics and on the boards of Forensic Science, Medicine and Pathology and the Australian Journal of Forensic Science . He is a sought-after expert witness and media commentator in forensic science.

Roy Dongمشاهده پروفایل

استادیار

• Control Theory
• Cyber-Physical Systems
• Machine Learning
• +۷ مورد دیگر

Roy Dong is an Assistant Professor at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research bridges Control Theory Economics Statistics Optimization to address challenges in cyber-physical systems and the Internet of Things, focusing on data manipulation, privacy, and strategic behavior in interconnected systems. His academic journey includes a Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2017) and dual B.S. degrees in Economics and Computer Engineering from Michigan State University (2010). At Illinois, he teaches courses ranging from Control Systems to Convex Optimization , with multiple teaching excellence awards. Roy's research explores Closed-loop effects of machine learning Causality in decision systems Incentive design for strategic agents Privacy-utility tradeoff optimization Human behavior modeling with applications in smart grids, transportation networks, and semi-autonomous vehicles. His work formulates privacy-preserving mechanisms as optimization problems, balancing data utility against user privacy in dynamic systems. Article trends show expertise in Game theory for strategic data sources Energy disaggregation techniques Nonlinear basis pursuit algorithms Privacy-aware control systems with a focus on cyber-physical systems and human-in-the-loop applications. Scientific recognition includes 'Teacher Ranked as Excellent' awards (ECE 120, ECE 486, ECE 515) Contributions to smartSDH building control and CPRL compressive sensing Roy leads the Privacy-aware Control Systems research group, collaborating with institutions like UC Berkeley and Michigan State University , and directs projects funded by grants including the New USDA NIFA grant for agricultural robot autonomy .

Reinhard Heckelمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Computational Imaging
• +۵ مورد دیگر

Reinhard Heckel is a Tenured Associate Professor (equivalent to Professor) of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM), and Adjunct Faculty in Electrical and Computer Engineering at Rice University. He was previously an Assistant Professor at Rice (2017–2019), a postdoc in the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley, and a researcher at IBM Research Zurich. Education: PhD, 2014 – ETH Zurich Visiting PhD student – Department of Statistics, Stanford University Research Interests: His work centers on machine learning and information processing with three major thrusts: (1) developing algorithms and theoretical foundations for deep learning, especially for accelerated magnetic resonance imaging ; (2) establishing rigorous mathematical and empirical underpinnings for modern machine-learning systems; and (3) leveraging DNA as a digital information-storage medium , including error-correction coding and system design for DNA-based storage. Across more than 100 peer-reviewed papers since 2017, Heckel’s research exhibits a strong interdisciplinary blend of computational imaging , machine-learning theory , and molecular data storage . Recent 2024–2025 publications show intensive focus on robust MRI reconstruction using diffusion priors, evaluation of bias in large web-text corpora, and state-of-the-art error-correcting codes for DNA storage channels. A forthcoming book, Deep Learning for Computational Imaging (Oxford University Press), consolidates his contributions to the field. Outreach & Media: Keynote and panel talks at DLD, TUM, and major ML conferences Op-eds in Frankfurter Allgemeine on ChatGPT and DNA storage Science features on Netflix, BBC, and German television (Galileo, “Gut zu Wissen”) Research Environment: At TUM he leads a group investigating theoretical and applied aspects of deep learning, compressed sensing, and coding for DNA storage. Open-source repositories on GitHub (e.g., dna_data_storage , supplement_deep_decoder ) provide code and data supplements accompanying his publications.

Istvan Albertمشاهده پروفایل

استاد پژوهشی

• Bioinformatics
• Genomics
• Computational Biology
• +۵ مورد دیگر

Istvan Albert is a Research Professor of Bioinformatics at Pennsylvania State University , affiliated with the Department of Biochemistry and Molecular Biology . He leads the Bioinformatics Consulting Center and teaches BMMB 852: Applied Bioinformatics . Research Interests: Specializing in bioinformatics, large-scale biological data analysis, microarray and sequence analysis, scientific programming, algorithm development, and database-driven web development. His work spans gene ontology visualization , RNA-Seq analysis , and coronavirus research . Software Development: Created GeneScape for gene function visualization and bio for bioinformatics workflows. Maintains the Biostar Handbook series and the Biostars Q&A Forum , a leading bioinformatics resource.

Lena Simineمشاهده پروفایل

دانشیار

• Theoretical Chemistry
• Chemical Physics
• Computational Materials Science
• +۳ مورد دیگر

Lena Simine is an Associate Professor in the Department of Chemistry at McGill University, affiliated with the Faculty of Science. She holds a B.Sc. (2009) and Ph.D. (2015) from the University of Toronto, followed by a postdoctoral fellowship at Rice University (2015–2019). Her laboratory is located in P&P 118A, focusing on developing computational approaches for modeling molecular phenomena in theoretical chemistry and chemical physics. Her research interests center on computational materials design, quantum dynamics, and the application of machine learning to chemistry. Specific areas include simulating amorphous materials, aptamer design, and quantum systems modeling. She teaches CHEM 365 (Statistical Thermodynamics) and CHEM 593 (Statistical Mechanics and Machine Learning for Chemistry). Her work explores interdisciplinary frontiers, such as path-integral simulations, GFlowNets for molecular design, and the physical principles underlying deep learning in materials science. Recent studies highlight innovations like DeltaGzip for binding affinity prediction and the MAP protocol for 3D disordered matter simulations. Her lab’s contributions span computational methods, material innovation, and quantum phenomena, with a focus on advancing both theoretical frameworks and practical applications in chemistry and materials science.