Associate Professor Judy Hart is a materials scientist at the School of Materials Science & Engineering, UNSW Sydney , specializing in the development of semiconducting materials for renewable energy applications. Her work integrates computational (DFT) and experimental approaches to understand composition-property relationships in systems like solid solutions , heterostructures , and doped materials for photocatalysis and solar cells . She leads projects funded by ARC Discovery and Linkage grants , including work on photo-electro-catalysis systems and stabilizing ceramic materials . Education: PhD in Materials Engineering (Monash University, 2007), BEng (Materials) (Monash, 2002) Professional Experience: Senior Lecturer (UNSW, 2017–), Lecturer (UNSW, 2013–2017), University of Bristol (2007–2012) Research Interests Her research focuses on designing materials for renewable energy , particularly photoelectrochemical water splitting and organic oxidation reactions . Key areas include Density Functional Theory (DFT) , defect engineering , band gap tuning , and nanostructured materials . She investigates ferroelectric polarization effects , metal oxide heterostructures , and stability of battery components , with applications in hydrogen production , CO2 conversion , and advanced battery materials . Scientific Awards Ramsay Memorial Fellowship (University of Bristol, 2007–2009) Teaching Contributions She is co-author of the 1st Australian & New Zealand edition of "Materials Science and Engineering: An Introduction" , and teaches courses on computational materials science , corrosion-resistant surfaces , mechanical behavior of metals , and materials design .
Dr. Todd D. Murphey is a Professor of Mechanical Engineering at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. He serves as Director of Transformative Research and Director of the Master of Science in Robotics Program at Northwestern, leading initiatives in computational dynamics, control systems, and robotics. His work bridges engineering, neuroscience, and biomedical applications, with a focus on developing systems that interact effectively with humans and their environments. Dr. Murphey received his Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 2002, with a thesis titled "Control of Multiple Model Systems." Prior to that, he earned a B.S. in Mathematics, summa cum laude, from the University of Arizona in 1997. Dr. Murphey's research centers on computational methods in dynamics and control, with applications spanning neuroscience, health science, robotics, and automation. His work in the Interactive & Emergent Autonomy Lab focuses on computational models of embedded control, biomechanical simulation, dynamic exploration, and hybrid control. The group develops mathematical approaches that lead to orders of magnitude improvement in computational efficiency for real-time implementation. Key application areas include assistive exoskeleton control, stabilization of energy networks, bio-inspired active sensing, entertainment robots, robotic exploration, and software-enabled stroke rehabilitation. Analysis of Dr. Murphey's recent publications reveals a strong emphasis on human-swarm interaction, algorithmic matter, and control of cyber-physical systems in uncertain environments. His work increasingly integrates information theory with physical systems, exploring how both autonomous and biological systems interact with environments to learn and improve behaviors. Recent trends show growing applications in rehabilitation technology, with particular focus on human-machine interaction in biomedical devices and embodied intelligence. Dr. Murphey has received numerous honors and awards for his contributions to robotics and engineering: Named Director of Transformative Research at Northwestern University (2025) Appointed IEEE Robotics and Automation Society Vice President of Publication Activities (2022) Co-recipient of Best Paper Award for IEEE Transactions on Robotics (2020) Appointed to Air Force Scientific Advisory Board (2019) Recipient of ABB Best Student Paper Award for CPL-SLAM research (2019) Cole-Higgins Award from Northwestern Engineering (2015) Dr. Murphey has supervised numerous graduate students including Taosha Fan, Giorgos Mamakoukas, and Ian Abraham, with research spanning robotic exploration using electrosense and mechanical contact, human-in-the-loop control, and shared control for rehabilitation devices. His lab has secured significant funding from the National Science Foundation, DARPA, and industry partners including Siemens and Ekso Bionics, supporting research in algorithmic matter, emergent behavior, and human-swarm collaboration. The Interactive & Emergent Autonomy Lab, led by Dr. Murphey, investigates how both autonomous systems and biological systems interact with their environments to learn and improve behaviors. Current projects include active learning and data-driven control, active perception in human-swarm collaboration, algorithmic matter and emergent computation, control for nonlinear and hybrid systems, cyber physical systems in uncertain environments, harmonious navigation in human crowds, information maximizing clinical diagnostics, reactive learning in underwater exploration, robot-assisted rehabilitation, and software-enabled biomedical devices. The lab collaborates with researchers across Northwestern and institutions including Georgia Tech, MIT, and industry partners.
Saeed Mehraban is an Assistant Professor of Computer Science at Tufts University's School of Engineering and an Assistant Professor in the Department of Physics & Astronomy within the School of Arts and Sciences. He joined Tufts University in June 2022 as an Assistant Professor after serving as a Visiting Assistant Professor from June 2021 to May 2022. Prior to his position at Tufts, he was an IQIM Postdoctoral Scholar at the California Institute of Technology and a research fellow at the Simons Institute for the Theory of Computing during spring 2020. Doctor of Philosophy in Electrical Engineering and Computer Science from MIT (2019) Master of Science from MIT (2015) B.Sc. in Physics from Sharif University of Technology, Iran (2013) B.Sc. in Electrical Engineering from Sharif University of Technology, Iran (2013) Saeed Mehraban's research focuses on quantum computation and information, exploring the profound connections between computer science and physics. His work particularly addresses quantum computational complexity and continuous variable systems. A significant portion of his recent research concerns delineating the boundary between classical and quantum computing in noisy intermediate-scale quantum devices. His research bridges theoretical computer science with quantum physics, examining fundamental questions about what quantum computers can and cannot efficiently solve, with particular emphasis on mathematical foundations and computational complexity aspects of quantum information processing. Mehraban's publication record demonstrates a strong focus on quantum computing theory, with particular emphasis on quantum complexity, quantum algorithms, and the mathematical foundations of quantum information. His recent work (2021-2023) has explored topics like unitary t-designs, holomorphic representations of quantum computations, and quantum-inspired identities. Earlier publications (2015-2020) examined computational complexity in quantum theories, approximation algorithms for matrix problems, and connections between classical algorithms and quantum many-body systems. His research consistently sits at the intersection of theoretical computer science and quantum physics, addressing fundamental questions about computational advantages of quantum systems. Gold Medalist, National Physics Olympiad (2007) Bronze Medalist, National Astronomy Olympiad (2005) Identified as Exceptional Talent by the Iranian Educational System (2004) Mehraban teaches dissertation research courses at Tufts University, indicating his involvement in mentoring graduate students. His teaching activities include specialized courses in quantum information science, quantum computer science, and quantum complexity theory. His professional activities show invitations to speak at prestigious institutions including Microsoft Research Station Q, Mila Institute in Quebec, and the Simons Institute, suggesting recognition of his research contributions. His postdoctoral work at Caltech's Institute for Quantum Information and Matter (IQIM) demonstrates his connection to leading quantum research groups. While specific lab affiliations at Tufts aren't explicitly detailed in the provided information, Mehraban's teaching of specialized quantum courses and his research profile suggest he likely contributes to quantum computing research initiatives at Tufts University. His background at Caltech's IQIM and involvement with the Simons Institute's Quantum Wave in Computing Program indicate strong connections to the broader quantum information science community.
Thomas E. Bittner serves as an Associate Professor in the Department of Philosophy at the University at Buffalo, specializing in formal and applied ontology with particular focus on spatial and temporal reasoning. His academic work bridges philosophy, computer science, and geographic information systems. His research interests encompass Formal and Applied Ontology , Ontology of Space, Time and Spatial Entities , Spatio-Temporal Reasoning , and Bio-Medical Ontology . He investigates fundamental questions about spatial relations, vagueness in geographic boundaries, and quantum-inspired models for geographic information systems, developing rigorous formal frameworks applicable to both philosophical and computational domains. His publication record demonstrates consistent engagement with cutting-edge topics in ontology, particularly exploring quantum geography concepts and computational implementations of formal ontologies. Key themes across his work include the representation of spatial vagueness, quantum-inspired geographic modeling, and verifiable computational ontology systems. Bittner holds a PhD from the Technical University of Vienna and teaches courses including Logic and Spatial Ontology. His contact information lists office location at 109 Park Hall on UB's North Campus with phone (716) 645-5149.
Tim Colonius is the Frank and Ora Lee Marble Professor of Mechanical Engineering and Medical Engineering and holds the Cecil and Sally Drinkward Leadership Chair at the California Institute of Technology. He has been affiliated with Caltech since 1994 and currently serves as Executive Officer for Mechanical and Civil Engineering . Colonius earned his B.S. from the University of Michigan (Ann Arbor), and both his M.S. and Ph.D. from Stanford University. Research Interests: His work focuses on fluid dynamics (global instabilities, cavitation, aerodynamic sound), flow control (closed-loop control, reduced-order modeling), and biomedical applications (shock waves, lithotripsy, ultrasound). He also develops advanced numerical methods for interface capturing, immersed-boundary techniques, and high-order accuracy. Scientific Contributions: Recent publications highlight his research in multiphase flows, vortex ring collisions, turbulent jet analysis, GPU-accelerated simulations, and biomedical applications. His group uses computational and data-driven approaches to study turbulence, instabilities, and flow optimization. Scientific Awards: AIAA Aeroacoustics Award Fellow of the Acoustical Society of America Fellow of the American Physical Society (APS) NSF and DoD research grants
Chris H. Wiggins serves as Associate Professor of Applied Mathematics at Columbia Engineering and Chief Data Scientist at The New York Times. He is a founding member of Columbia's Data Science Institute executive committee and Department of Systems Biology, with additional affiliations in Statistics, Foundations of Data Science, Health Analytics, Computational Social Science, and Education. His educational background includes: PhD in Theoretical Physics from Princeton University (1993-1998) Courant Instructor position at NYU (1998-2001) Wiggins' research bridges data science ethics , systems biology , and applied mathematics , focusing on societal implications of technology. His work examines disinformation ecosystems, ethical product design frameworks, and algorithmic accountability through interdisciplinary lenses combining computational methods with social science perspectives. His publications reveal a consistent emphasis on ethical infrastructure in data science, particularly how social media platforms can implement Tukey-inspired principles through measurable objectives. This reflects his broader mission to operationalize ethics in technology development. Award highlights: Fellow of the American Physical Society Columbia’s Avanessians Diversity Award Wiggins co-founded hackNY (2010), establishing semesterly student hackathons and the Fellows Program connecting students with NYC startups. His advisory work focuses on creating structured pathways for academic-industry collaboration in data science education. He leads Columbia's Computational Social Science initiatives and hackNY's educational infrastructure, fostering communities where students develop real-world data science solutions while engaging with ethical challenges.
Sanjeev Baskiyar is a Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. He has been actively involved in research, teaching, and academic leadership, with a strong focus on computer systems, real-time and embedded computing, scheduling, cloud and fog computing, and energy-aware architectures. Education: Ph.D., Electrical and Computer Engineering, University of Minnesota M.S., Electrical and Computer Engineering, University of Minnesota B.S., Electronics and Communications, Indian Institute of Science, Bangalore B.S., Physics (with honors), and distinction in Mathematics Dr. Baskiyar’s research interests span scheduling, real-time and embedded systems, computer architecture, fog/cloud computing, thermal/energy-aware computing, and STEM education. His recent work explores machine learning applications in scheduling and quantum computing for fake news detection. He has supervised over 25 graduate students, many of whom now hold academic and industry positions. His recent publications emphasize fog computing simulation, service placement, quantum-inspired fake news detection, and adaptive scheduling using machine learning. These works reflect a trend towards intelligent, scalable, and energy-efficient computing systems, particularly in distributed and edge environments. Scientific Awards and Honors: Walker Teaching Excellence Award, Auburn University, 2020 Summer Faculty Fellow, Air Force Research Labs, 2020 Nominated Best Teaching Assistant, University of Minnesota, 1992 Multiples Merit and State-merit Scholarships Honors in Physics and Distinction in Mathematics Dr. Baskiyar has successfully advised numerous MS and PhD students and secured over $2 million in research funding as Principal Investigator from the National Science Foundation, DARPA, NASA, and industry partners like Wind River Systems and Mentor Graphics. His grants focus on parallel computing education, real-time micro-architectures, and embedded systems. He has also served on editorial boards, program committees, and as a reviewer for NSF and IEEE journals. He has held leadership roles including Senator in the University Faculty Senate and Chair of the E-day Committee. Labs and Research Groups: While not explicitly named, Dr. Baskiyar leads a research group focused on computer systems, scheduling, and embedded computing, as evidenced by his long list of graduate student supervision and funded projects in fog, cloud, and real-time systems.
Donald Spector is Professor of Physics at Hobart and William Smith Colleges (HWS), where he has been a faculty member since 1989. He holds a Ph.D. in Physics from Harvard University (1986) and has taught at Harvard, Cornell, and the University of Utrecht. He is affiliated with the Department of Physics in the School of Natural and Social Sciences and has served as coordinator of the Engineering Program and chair of the Physics Department. Ph.D., Harvard University, 1986 A.M., Harvard University, 1983 A.B., Harvard University, 1981, magna cum laude His research centers on supersymmetry, quantum field theory, and mathematical physics, with significant contributions to Q-balls, magnetic monopoles, and duality in supersymmetric quantum mechanics. He explores the intersection of physics with number theory, set theory, and computational complexity. His interdisciplinary work spans physics and the arts, particularly music (e.g., John Cage, Terry Riley) and theatre (e.g., Waiting for Godot ). His recent publications reveal a strong trend toward foundational questions in physics and information theory, especially the application of set-theoretic forcing to generalize information theory. His work bridges theoretical physics, mathematics, and the humanities, often drawing analogies between physical principles and artistic expression. Scientific awards and honors include: Teaching awards at Harvard and Cornell NSF-NATO Postdoctoral Fellowship KITP Scholar (2005–2008) Japan Society for the Promotion of Science Visiting Fellowship Philip J. Moorad Professor of Science (2005–2010) FQXi Grant (2013–2015) Spector has been regularly funded by the National Science Foundation, FQXi, KITP, and JSPS. He has supervised student research in quantum mechanics and simulated annealing. He is a founding member and board member of the Anacapa Society, which promotes theoretical physics at undergraduate institutions. He teaches courses such as Quantum Computing, Modern Physics, and interdisciplinary seminars like Physics through Star Trek and Time Travel & Multiple Universes . He is involved in multiple labs and collaborative initiatives, including organizing workshops at the Kavli Institute for Theoretical Physics and contributing to interdisciplinary projects at the Institute for Science and Interdisciplinary Studies. His recent work includes performing in plays and providing dramaturgical support for theatre productions.
Dr. Sheng Yang is an Assistant Professor in the School of Engineering at the University of Guelph. He leads the Design Innovation and Intelligent Manufacturing (DIIM) lab, focusing on advancing additive manufacturing, generative design, and smart manufacturing technologies. His research integrates IoT, big data analytics, and bio-inspired design to address challenges in aerospace, green energy, and healthcare. Key areas include computational design for additive manufacturing, data-driven mass customization, and digital twin-based optimization. Education: Ph.D. in Mechanical Engineering from McGill University (2019), followed by a Postdoctoral Fellowship at McGill (2019–2020). Joined University of Guelph in 2020. Research interests span energy efficiency, complex system optimization, and personalized healthcare products. Recent work emphasizes digital twin synchronization in robotics, machine learning for quality prediction, and sustainable additive manufacturing processes. Notable awards include the 2019 Association of Commonwealth Universities Blue Charter Fellowship and 2018 ASME Best Paper Award. His lab actively seeks partnerships in personalized healthcare, product design, and smart manufacturing. Grants and collaborations focus on advancing manufacturing technologies and sustainability. No formal advisees listed, but active in graduate training through lab projects. The DIIM lab explores cutting-edge solutions for industrial and societal challenges through interdisciplinary approaches.
Hugh Churchill is a Professor in the Department of Physics at the University of Arkansas, College of Arts & Sciences. His research focuses on quantum materials and devices, particularly condensed matter physics with applications in 2D systems and quantum transport. Education: PhD in Physics from Harvard University, BA in Physics and BM in Music Performance from Oberlin College Recent research trends include studies on 2D materials like transition metal dichalcogenides and black phosphorus, investigating quantum transport phenomena, supercurrent tuning, strain engineering for exciton control, and applications of machine learning in quantum material discovery. His work also explores THz emission mechanisms and quantum noise mitigation strategies. Arkansas Research Alliance Fellow Presidential Early Career Award for Scientists and Engineers NSF CAREER Award ORAU Powe Junior Faculty Award AFOSR Young Investigator Connor Faculty Fellowship Hugh teaches graduate and undergraduate courses in quantum mechanics, modern physics, and 2D materials, including PHYS 5413 Quantum Mechanics I and PHYS 6713 Condensed Matter Physics II.
Silas Alben is a Professor in the Department of Mathematics at the University of Michigan, affiliated with the College of Literature, Science, and the Arts. His research focuses on applied mathematics and mathematical biology, particularly fluid-structure interactions in biological systems. He employs computational simulations and laboratory experiments to study fundamental physics of flexible bodies in fluids. Research interests include biomechanics of swimming organisms, vortex dynamics in fluid-structure interactions, and thermal transport optimization. His work bridges mathematical modeling with experimental validation to understand complex physical phenomena. Publications demonstrate strong focus on fluid dynamics applications, including vortex-enhanced heat transfer, membrane flutter dynamics, and bio-inspired locomotion. Recurring themes include optimization of fluid-structure systems, vortex wake interactions, and computational methods for aeroelastic problems.
Dr. Andrew Erwin is an Assistant Professor in Mechanical Engineering at the University of Cincinnati, focusing on robotics, human-robot interaction, and rehabilitation engineering. He holds a PhD and MS from Rice University (2018, 2014) and a BS from the University of Massachusetts Amherst (2012). Prior to UC, he was a postdoc at the University of Southern California and the Jet Propulsion Laboratory. His research explores how forces and movements are executed in healthy individuals, and how robotic devices can assist or restore function post-injury. Key areas include rehabilitation robotics, bio-inspired systems, haptic interfaces, and motor learning. He has received prestigious awards such as the NASA Postdoctoral Program Fellowship (2018) and the IEEE/ASME Transactions on Mechatronics Best Paper Award (2017). Dr. Erwin’s work integrates biomechanics, control systems, and neurophysiology. His lab develops devices like the SE-AssessWrist for wrist assessment and explores planetary seismometers for space missions. He maintains an active Google Scholar profile with over 25 publications. Education: PhD, Mechanical Engineering, Rice University, 2018 MS, Mechanical Engineering, Rice University, 2014 BS, Mechanical Engineering, University of Massachusetts Amherst, 2012 His current research emphasizes curriculum design for robotics learning, human-robot collaboration, and adaptive control systems. He offers a PhD position for Fall 2025 focusing on these areas.
Subhabrata Sen is an Assistant Professor of Statistics at Harvard University, located in Science Center 713, Cambridge. His research focuses on Applied Probability, Statistics of Networks, Signal Detection, and Machine Learning. He holds a PhD from Stanford University (2017), advised by Amir Dembo and Andrea Montanari, and prior degrees from the Indian Statistical Institute, Kolkata. His work bridges statistical theory, high-dimensional data analysis, and applications in networks and physics-inspired methods. Key contributions include foundational studies on spin glasses, community detection, and causal inference in complex systems. His research often employs mean-field techniques and explores universality principles in estimation problems. Selected awards and recognition are not explicitly mentioned in the provided text. His advising and grants include postdoctoral mentoring at Microsoft Research and MIT (2017-19). He collaborates on projects involving spectral methods, random matrix theory, and multi-layer network analysis. Labs/teams: Active in Harvard's Statistics Department research groups focused on statistical theory and network science. Maintains an academic website with preprints and resources.
Dr. Youngchan Kim is a Lecturer in Quantum Biology at the University of Surrey , serving as Director of the Quantum Biology Doctoral Training Centre (QB-DTC). He is affiliated with multiple departments including the School of Biosciences, Advanced Technology Institute, and Quantum Sciences Group. PhD in Physics (2011), Korea Advanced Institute of Science and Technology MSc in Physics (2008), KAIST BSc in Physics (2006), Chung-Ang University Graduate Certificate in Learning and Teaching (2022), Advance HE His research focuses on quantum phenomena in biological systems at physiological temperatures, particularly using femtosecond optical spectroscopy and genetically engineered fluorescent proteins to explore evolutionary adaptations and develop quantum-bio-inspired technologies like room-temperature single-photon sources. The 15 most recent publications span quantum biology, biophotonics, and optical spectroscopy, with particular emphasis on quantum coherence in biological systems , terahertz birefringence , fluorescent protein dynamics , and biomedical imaging innovations . These works demonstrate his interdisciplinary approach bridging physics, biology, and medical applications. As QB-DTC Director, he leads transdisciplinary initiatives fostering collaboration between quantum physics and biosciences. His technical expertise includes time-correlated single-photon counting , common-path interferometry , and ultrafast fluorescence depolarization techniques.
Alexei Koulakov is a Professor at Cold Spring Harbor Laboratory (CSHL) and the Charles Robertson Professor of Neuroscience. His research focuses on applying mathematical and computational approaches to unravel the principles of brain organization, particularly in sensory systems like olfaction and vision. Koulakov's work explores how neural circuits form during development, the role of genetic and experiential factors, and the evolutionary basis of brain architecture. Education: PhD in Physics from the University of Minnesota (1998). Key Research Areas: Olfactory system development, neural network modeling, and AI inspired by biological computation. Koulakov's recent publications emphasize cross-disciplinary integration of neuroscience and AI, including NeuroAI initiatives and DeepNose models predicting olfactory percepts. His team investigates how innate abilities are encoded genomically and how experience shapes neural networks. Scientific contributions include studies on primacy coding in olfaction, stochastic learning mechanisms , and high-throughput neural mapping . Awards include the Charles Robertson Professorship , reflecting his leadership in theoretical neuroscience. Koulakov collaborates extensively, with notable work on genomic bottlenecks , odor mixture interactions , and neural integrator models . His lab at CSHL is at the forefront of NeuroAI research, leveraging brain circuit insights to advance artificial intelligence.
