Prof. Martin Haenggi is the Frank M. Freimann Professor of Electrical Engineering and Concurrent Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He holds a Dr.sc.techn. (Ph.D.) from ETH Zurich and has been at Notre Dame since 2000. His research focuses on stochastic geometry and wireless networks, including cellular, heterogeneous, vehicular, and millimeter-wave systems. He has held sabbaticals at UCSD (2007–2008), EPFL (2014–2015), and ETH Zurich (2021–2022). Education: Dipl.-Ing. (M.Sc.), ETH Zurich, 1995 Dr.sc.techn. (Ph.D.), ETH Zurich, 1999 Research interests emphasize stochastic geometry for analyzing network performance, including coverage, interference, and reliability in wireless systems. Key areas include meta distributions, spatial-temporal analysis, and network optimization. His work has been recognized with IEEE Fellow status, Clarivate Highly Cited Researcher distinction, and NSF CAREER Award (2005). Grants and Awards: NSF Award (Deep Stochastic Geometry: 2020–2023) NSF Award (Toward a Stochastic Geometry for Cellular Systems: 2015–2019) Rice Prize (2017), Best Survey Paper Award (2017), and Best Tutorial Paper Award (2010) from IEEE Communications Society Teaching includes advanced courses on stochastic geometry, wireless networks, and signal processing. His lab focuses on theoretical and applied aspects of network modeling, with collaborations in industry and academia.
Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.
Markus Heinonen is an Academy Research Fellow at Aalto University's Department of Computer Science within the School of Science. His academic position is tied to Harri Lähdesmäki's Professorship, focusing on probabilistic machine learning. He holds a Doctoral degree in Engineering and Technology from the University of Helsinki (2013). His research integrates probabilistic modeling , deep learning , and differential equations , with applications in computational biology, drug discovery, and biophysics. Key themes include Gaussian processes, Bayesian inference, generative models, and their use in understanding complex biological systems like immune cell behavior (e.g., T cell receptor analysis in aplastic anemia) and molecular design. He leads major projects such as the Deep Learning with Differential Equations initiative (2020–2025), exploring continuous-time models and physics-informed neural networks. His work bridges theory and application, evidenced by collaborations in diffusion models , optimal transport , and single-cell analysis . Publications span over 65 peer-reviewed outputs, with recent emphases on robust neural network training, multi-target molecular prediction, and interpretable drug design frameworks. His research contributes to UN Sustainable Development Goal 3 (Good Health) through advancements in disease modeling and therapeutic development. He has undertaken visiting research roles at the University of California, San Francisco (2017) and Telecom ParisTech (2013–2014). Media highlights include recognition for work on TCR-epitope prediction and AI-driven enzyme engineering.
Risto Miikkulainen is a Professor of Computer Science and Neuroscience at the University of Texas at Austin and VP of AI Research at Cognizant AI Lab. He directs the UTCS Neural Networks Research Group and is currently on leave from UT, working on Evolutionary Computation and Deep Learning at Sentient Technologies, Inc. Education: Ph.D. in Computer Science, UCLA, 1990 M.S. in Applied Mathematics, Helsinki University of Technology (now Aalto University), 1986 Risto Miikkulainen's research focuses on biologically-inspired computation such as neural networks and evolutionary computation. His work spans three main areas: (1) Neuroevolution, evolving complex deep learning architectures and recurrent neural networks for sequential decision tasks in robotics, games, and artificial life; (2) Cognitive Science, developing models of natural language processing, memory, and learning that shed light on disorders such as schizophrenia and aphasia; and (3) Computational Neuroscience, studying the development, structure, and function of the visual cortex, episodic memory, and language processing. His research combines theoretical understanding of biological information processing with practical applications for developing intelligent artificial systems. His recent publications (2025) show a strong focus on evolutionary approaches to AI development, particularly in neural architecture search, loss function optimization, and explainable AI. Many papers explore the intersection of evolutionary computation with deep learning, creating more efficient and transparent AI systems. His work spans theoretical foundations and practical applications in areas ranging from environmental control systems to cognitive modeling. Scientific Awards: College of Fellows, International Neural Network Society, 2024 Best Pathway to Impact Award, NeurIPS Climate Change workshop, 2024 AAAI Fellow, 2023 IEEE CIS Evolutionary Computation Pioneer Award, 2020 Gabor Award, International Neural Network Society, 2017 Outstanding Paper of the Decade Award, International Society for Artificial Life, 2017 IEEE Fellow, 2016 Multiple Best Paper Awards at GECCO, CIG, and CEC conferences Deployed Application Award, AAAI/IAAI-2013, AAAI/IAAI-2018 Miikkulainen has extensive experience mentoring students through undergraduate research courses like CS378 Computational Intelligence in Game Design I and II, where students develop independent research projects on the OpenNERO research platform. He has received multiple awards for deployed applications, demonstrating the practical impact of his research. His work has led to the development of the NERO game platform, which serves as both an educational tool and research platform for AI. He directs the UTCS Neural Networks Research Group, which focuses on neuroevolution, cognitive science models, and computational neuroscience. The group has developed the NERO (Neuro-Evolving Robotic Operatives) platform, a machine learning game that allows users to train intelligent agents through evolutionary computation. The group's work spans theoretical research and practical applications in AI, with connections to both academic and industry partners.
Silvia Santos is a Group Leader at the Francis Crick Institute, leading the Quantitative Stem Cell Biology Lab since January 2018. Her research focuses on understanding cell decision-making during transitions, specifically cell division and differentiation in early development using human embryonic stem cells. She combines experimental techniques with theoretical approaches, including advanced microscopy, genomics, and computational modeling. Education and Career: PhD in Molecular and Cell Biology from EMBL-Heidelberg (2008), followed by postdoctoral training at Stanford University (2009-2014). She held an MRC Career Development Award at Imperial College London (2014-2017) before joining the Crick. Her work emphasizes interdisciplinary methods to study cellular processes in health and disease. Research Interests: Spatial-temporal control in cell decisions, stem cell differentiation, cell cycle regulation, and modeling embryonic development. She advocates for women in science and mentorship programs for early-career researchers. Key Achievements: Recipient of Marie Curie E-Star, EMBO, and HFSP fellowships. Recognized with the BioModels’ Model of the Year 2023 for contributions to systems biology. Her lab develops models like gastruloids to study embryonic development. Grants and Mentorship: Supported by MRC and other grants. Committed to fostering excellence in training and mentorship, previously chairing mentorship initiatives at Imperial College London. Labs and Teams: Quantitative Stem Cell Biology Lab at the Crick, collaborating with interdisciplinary teams on projects involving proteomics, genomics, and high-throughput screening.
Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .
Dr. Igor V. Pivkin is a Full Professor at the Institute of Computing within the Faculty of Informatics at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His academic journey includes degrees from Novosibirsk State University (B.Sc./M.Sc. Mathematics), Brown University (M.Sc. Computer Science and Ph.D. Applied Mathematics), and postdoctoral research at MIT's Department of Materials Science and Engineering. His research focuses on multiscale/multiphysics modeling , numerical methods , and large-scale simulations of biological and physical systems. Key areas include biophysics, cellular/molecular biomechanics, stochastic modeling, and coarse-grained molecular simulations. He leverages high-performance computing (HPC) and particle-based methods to address complex biological phenomena. His work spans diverse applications, from understanding cellular mechanosensitivity and biofilm engineering to modeling cancer cell behavior and red blood cell dynamics in the spleen. His contributions bridge computational science, biotechnology, and biomedical research. He has published extensively in top-tier journals, with recent work advancing automated biofilm analysis, deep learning for microbial classification, and systems biology approaches to metal bioleaching. His lab collaborates on interdisciplinary projects, emphasizing computational innovation for real-world biological challenges.
David Sivak is a Professor and Canada Research Chair Tier 2 in the Department of Physics at Simon Fraser University (SFU). His research focuses on theoretical and computational biophysics, exploring the physical limits of energy and information transduction in molecular machines. He investigates how biological systems operate efficiently under fluctuating conditions, with emphasis on molecular motors, free-energy transduction, and nonequilibrium thermodynamics. Education: Ph.D. in Biophysics, University of California, Berkeley B.A. in Philosophy, Politics & Economics, University of Oxford A.B. with Honors in Applied Mathematics, Harvard University Research Interests: Sivak's work integrates statistical mechanics, information theory, and molecular biophysics to understand how microscopic systems achieve functional efficiency. Key areas include: Optimal control strategies for nonequilibrium systems Energy-information trade-offs in molecular machines Stochastic thermodynamics of biochemical processes Design principles of ATP synthase and similar rotary motors Publications & Awards: Sivak has authored over 60 peer-reviewed articles, including foundational work on nonequilibrium fluctuation theorems and optimal control in stochastic systems. His awards include the Canada Research Chair and the Young Investigator Award from the Biophysical Society of Canada. Grants & Collaborations: He leads projects funded by the Simons Foundation and collaborates with experimental groups globally. His lab actively mentors graduate students and postdocs, emphasizing interdisciplinary approaches to biophysical problems. Labs & Teams: The Sivak Group at SFU develops theoretical models and numerical simulations to analyze molecular-scale processes. Current projects explore information engines, microbiome dynamics, and the thermodynamics of cellular processes.
Dr. Appala Raju Badireddy is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Vermont (UVM), and Director of the Water Treatment & Environmental Nanotechnology (WTEN) Laboratory. He is also co-founder and CTO of Secure Surgical Solutions LLC, and a founding member of Vermont Initiative for Biological and Environmental Surveillance (VIBES). His research focuses on sustainable membrane processes, environmental nanotechnology, nanometrology, and water security. Education: Ph.D., Environmental Engineering, University of Houston (2003-2009) M.Tech., Chemical Engineering, Indian Institute of Technology Madras (2001-2003) B.Tech., Chemical Engineering, Jawaharlal Nehru Technological University Hyderabad (1997-2001) Postdoctoral Research, Duke University (2009-2014) under Prof. Mark Wiesner Research Interests: Sustainable Membrane Processes: Water/wastewater treatment, desalination, anti-fouling strategies, and resource recovery. Environmental Nanotechnology: Nano-enabled sensors, remediation, and implications of nanomaterials in ecosystems. Environmental Chemodynamics: PFAS fate/transport, nutrient cycling, and contaminant toxicity. Water Security: Real-time monitoring systems and soil health assessments. His work integrates lab-scale innovations with field applications, emphasizing interdisciplinary collaboration. Recent Research Trends: Recent publications highlight advancements in PFAS remediation, electric-field enhanced filtration, and living lab approaches to precision agriculture. He explores nanomaterials for water treatment while addressing their environmental implications through novel detection methods like ED-HSI microscopy. Labs & Initiatives: WTEN Lab: Focuses on nanotechnology-driven water solutions. VIBES: Develops environmental surveillance tools for public health. Secure Surgical Solutions: Applies nanotechnology to medical devices.
Béla Bollobás is a renowned mathematician affiliated with the University of Memphis as the Jabie Hardin Chair of Excellence in Combinatorics and the University of Cambridge as a Fellow of Trinity College and Honorary Professor at the Centre for Mathematical Sciences. His work spans combinatorics, probability theory, and graph theory, with significant contributions to percolation and random graphs. Dr. Rer. Nat. (Budapest, 1967) Ph.D. (Cambridge, 1972) Sc.D. (Cambridge, 1984) Bollobás pioneered extremal graph theory, random graphs, and probabilistic combinatorics. He introduced novel graph polynomials and advanced bootstrap percolation models, impacting both theoretical mathematics and statistical physics. His research includes inhomogeneous random graphs and cellular automata in random environments. His selected publications reveal a focus on percolation thresholds, graph invariants, and stochastic processes. Notably, he derived sharp thresholds for bootstrap percolation and defined critical probabilities for Voronoi percolation. Senior Whitehead Prize (2007) Fellow of the Royal Society (2011) Foreign Member, Hungarian Academy of Sciences (1990) Foreign Member, Polish Academy of Sciences (2013) Honorary Doctorate, Adam Mickiewicz University (2013) Szechenyi Prize (2017) Bollobás has supervised over 50 Ph.D. students and authored over 450 publications, including 10 books. He co-founded the journal Combinatorics, Probability and Computing and served on eight editorial boards. He organized numerous conferences, including Bill Tutte and Paul Erdős events.
James C. Gumbart is an Adjunct Professor in the School of Physics at Georgia Institute of Technology, with additional affiliation to the School of Chemistry and the Institute for Bioengineering and Bioscience . His research leverages molecular dynamics simulations to decode the atomic-level mechanisms of bacterial proteins and cellular structures. B.S., Physics and Mathematics, Western Illinois University, 2003 Ph.D., Physics, University of Illinois at Urbana Champaign, 2009 Dr. Gumbart's work bridges computational biophysics and biochemistry to understand: Mechanisms of bacterial membrane protein insertion and nutrient import Structural dynamics of cell wall mechanics SARS-CoV-2 spike protein interactions with ACE2 Free-energy calculations for protein-ligand binding Applications of machine learning in biomolecular simulations His publications reflect trends in membrane protein biophysics , viral dynamics , and computational drug design , with a strong emphasis on interdisciplinary techniques. Awards include multiple fellowships and grants from NSF , DOE , and NIAID . He has mentored numerous PhD students, including Zijian Zhang , David Ryoo , and Andrew Pang , whose work has advanced understanding of bacterial systems and viral proteins. The Gumbart Lab integrates high-powered supercomputing and advanced software to model biomolecular processes, fostering collaborations with institutions like the National Institutes of Health and Argonne National Laboratory .
Jennifer Lewis is the Hansjorg Wyss Professor of Biologically Inspired Engineering and Jianming Yu Professor of Arts and Sciences at Harvard University's Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS). Her research focuses on bioengineering, materials science, and advanced manufacturing, with emphasis on 3D-printed functional materials, organoids, and soft robotics. She leads the Lewis Research Group, which develops biomimetic technologies for regenerative medicine, energy systems, and robotics. Lewis holds appointments in SEAS, the Department of Chemistry and Chemical Biology, and the Wyss Institute for Biologically Inspired Engineering. Her research areas include applied mathematics, fluid mechanics, soft matter physics, and bioengineering applications such as kidney organoid models, vascularized tissues, and programmable materials. Notable innovations include kidney organoid-on-chip systems for drug testing, 3D-printed liquid crystal elastomers, and bioprinted cardiac tissues. Lewis was awarded the 2025 James Prize in Science and Technology Integration for pioneering interdisciplinary research. Her lab's projects span organ building blocks, immune-response modeling in transplanted tissues, and acoustophoretic printing techniques for high-resolution bioprinting. Collaborations include the NIH Somatic Cell Genome Editing Program and industry partnerships for bioprosthetic valve research. She advises on grants totaling over $20M and mentors a multidisciplinary team of postdocs and graduate students in materials science, biomedical engineering, and mechanical engineering. Lewis' lab facilities include the Pierce Hall lab (Cambridge) and Allston SEAS campus, with state-of-the-art 3D printing systems, microfluidics platforms, and bioreactors for organoid culture. Current projects aim to engineer functional human tissues for therapeutic applications and develop smart materials with programmable mechanical/chemical responses.
Dr. Craig S. Levin is a Professor of Radiology at Stanford University's Molecular Imaging Program at Stanford (Nuclear Medicine), with courtesy appointments in Physics, Electrical Engineering, and Bioengineering. He also holds memberships in Bio-X, the Cardiovascular Institute, the Wu Tsai Human Performance Alliance, and the Stanford Cancer Institute. Dr. Levin received his B.S. Summa Cum Laude in Physics and Mathematics from UCLA in 1985, followed by M.S., M.Phil., and Ph.D. degrees in Physics from Yale University in 1987 and 1993. His educational achievements were recognized with multiple honors including Phi Beta Kappa, Sigma Pi Sigma, and various departmental awards at UCLA. Dr. Levin's research focuses on the development of novel instrumentation and software algorithms for molecular imaging. His work spans medical physics, biomedical engineering, and instrumentation development with specific emphasis on positron emission tomography (PET), gamma camera technology, and multimodal imaging systems. His laboratory explores new concepts in radiation detection, image reconstruction algorithms, and the application of these technologies to cancer, heart disease, and neurological disorders. A notable aspect of his research involves pushing the physical limits of sensitivity and spatial, spectral, and/or temporal resolutions in imaging systems. His recent publications demonstrate a strong focus on enhancing PET technology, particularly time-of-flight capabilities, with significant work on improving coincidence timing resolution, developing MR-compatible PET systems, and applying deep learning techniques to image reconstruction and normalization. His research shows a clear trajectory toward higher resolution imaging with improved quantitative accuracy for both clinical and preclinical applications. Dr. Levin's scientific achievements have been recognized with numerous awards: American Institute for Medical and Biological Engineering's College of Fellows Academy of Radiology Research Distinguished Investigator Recognition Award National Research Service Award from NIH (1993-5) Pilot Research Award from the Society of Nuclear Medicine (1996) Multiple honors from UCLA including Phi Beta Kappa and Sigma Pi Sigma Full Tuition and Research Fellowship and Bates Graduate Fellowship from Yale University As an educator and mentor, Dr. Levin directs the NIH-NCI funded T32 Stanford Molecular Imaging Scholars postdoctoral training program and serves as a Doctoral Dissertation Advisor for students in Bioengineering and Biophysics. He currently advises five postdoctoral scholars and three doctoral candidates. His laboratory, the Molecular Imaging Instrumentation Laboratory, comprises approximately 20 members who work on developing new imaging technologies and translating them into clinical applications. Dr. Levin has secured substantial NIH funding as Principal Investigator along with grants from other government agencies, industry partners, and private institutions to support his research program. Dr. Levin's Molecular Imaging Instrumentation Laboratory is at the forefront of developing new imaging technologies that bridge physics, engineering, and medicine. The lab focuses on creating instrumentation for in vivo imaging of cellular and molecular signatures of disease, with particular emphasis on pushing the physical limits of imaging performance. Their work spans computer modeling, sensor development, electronics design, data acquisition systems, and advanced image processing algorithms. The lab maintains strong industry partnerships to translate their innovations into products used for patient care worldwide.
Michael Schaub is a tenure-track Assistant Professor in the Department of Computer Science at RWTH Aachen University, specializing in Computational Network Science. His research focuses on analyzing complex systems through network and graph models, integrating dynamical systems, control theory, and machine learning. He leads the Computational Network Science group, advancing methodologies for higher-order network models like simplicial complexes and hypergraphs. Schaub holds a PhD from Imperial College London and has held postdoctoral positions at MIT and Oxford. He is an ERC Starting Grant recipient (2022) and a Marie Curie Fellow, recognized for contributions to network dynamics and topological data analysis. Education: PhD in Mathematics, Imperial College London (2011-2015) MSc in Biomedical Engineering, Imperial College London (2010) BSc in Electrical Engineering, ETH Zurich (2007-2010) Research Interests: Schaub’s work spans interdisciplinary applications of network science, including biological systems, social networks, and technical infrastructures. Key areas include: Higher-order network models (hypergraphs, simplicial complexes) Graph signal processing and dynamics on networks Community detection and dynamical systems analysis Topological data analysis and machine learning Grants & Awards: ERC Starting Grant (2022): HIGH-HOPeS project Marie Skłodowska-Curie Fellowship (2017-2019) Junior Fellow, German Informatics Society (GI) Member of Junges Kolleg (North Rhine-Westphalia Academy) Labs & Teams: Leads the Computational Network Science Lab at RWTH Aachen, collaborating internationally on projects like the ELLIS Society and the European Laboratory for Learning and Intelligent Systems (ELLIS). Active in organizing workshops (e.g., Toponets, SIAM MDS).
David Doty is a Professor in the Department of Computer Science at the University of California, Davis . His research focuses on the intersection of molecular systems and computation , exploring how natural processes like chemical reactions , DNA nanotechnology , and self-assembly can perform computation. He also investigates connections to theoretical computer science, including distributed computing and algorithmic information theory . Doty's work bridges physics , chemistry , and biology through rigorous computational models like the Tile Assembly Model and Population Protocols . His research program includes software development ( scadnano , ppsim ), theoretical analysis, and collaborations with experimentalists. He teaches courses on theory of computation and molecular computing , and has advised numerous students in his research group. His publications cover topics such as algorithmic self-assembly , chemical reaction networks , and thermodynamic binding networks , with a focus on understanding fundamental computational and physical limits. Key software tools developed by his group include scadnano for DNA design and ppsim for population protocol simulations. Doty's recent research trends explore rate-independent chemical computing , error correction , and stochastic modeling in molecular systems. Current academic activity includes teaching ECS 120 (Undergraduate Theory of Computation), ECS 220 (Graduate Theory of Computation), and ECS 232 (Theory of Molecular Computation). He maintains a research lab in 2306 Academic Surge and continues to publish in leading conferences like DNA Computing and CMSB .
