Dr. Peichen Zhong is an Assistant Professor in the Department of Materials Science and Engineering at the National University of Singapore (NUS). He leads the Applied Machine Learning and Materials Modeling (AM³) Group, focused on advancing computational methods for clean energy technologies. His research integrates machine learning with atomistic simulations to tackle challenges in battery materials, disordered materials, and sustainable energy systems. Education: B.S. in Physics from University of Science and Technology of China (2018); Ph.D. in Materials Science from UC Berkeley (2023, advised by Prof. Gerbrand Ceder); Postdoctoral training at Lawrence Berkeley National Lab and BIDMaP, co-advised by Persson, Cheng, and Krishnapriyan. Research Interests: Computational modeling of battery cathodes/electrolytes, AI-driven interatomic potentials, statistical mechanics in disordered materials, and generative models for scientific discovery. Key areas include Li/Na-ion batteries, solid-state reactions, and sustainable energy materials. Awards: BIDMaP Emerging Scholar Fellowship (UC Berkeley CDSS, 202?), 2023 Rising Stars in Materials Science (CMU/MIT/Stanford). Labs/Teams: The AM³ Group at NUS MSE focuses on interdisciplinary research combining theory, computation, and AI4Science. Current openings include PhD students and postdoctoral researchers.
Raul Vicente Zafra is a Professor of Data Science at the University of Tartu, Faculty of Science and Technology, Institute of Computer Science, where he has been working since 2013. His research spans computational neuroscience, artificial intelligence, and data science, with a particular focus on bridging biological and artificial models of intelligence. Education: PhD in Physics (2001-2006), University of the Balearic Islands BSc in Physics (1997-2001) Professor Zafra's research interests center on computational neuroscience and artificial intelligence, with specific expertise in brain-computer interfaces, reinforcement learning, neural modeling, and explainable AI. His work bridges the gap between biological and artificial intelligence systems, exploring how neural principles can inform machine learning algorithms and vice versa. He has made significant contributions to understanding neural coherence, time interval learning in neural systems, and the application of information theory to brain-computer interfaces. His research often involves interdisciplinary collaboration between computer science, neuroscience, and medicine. Analysis of Zafra's recent publications reveals a strong focus on the intersection of artificial intelligence and neuroscience. His work spans explainable AI methods, brain-computer interfaces, reinforcement learning models that mimic cognitive processes, and neurophysiological studies of brain activity. A notable trend is his exploration of how biological principles of neural computation can inform and improve artificial intelligence systems, particularly in areas like time-based learning, consciousness modeling, and neural coherence. Scientific Awards: 2012: Attendee at the 62nd Lindau Nobel Laureate Meeting 2007: Quantum Electronics and Optics Division Prize of the European Physical Society for the best PhD Thesis in Applied Optics in Europe 2006: PhD Extraordinary Award of the Physics Department of the University of the Balearic Islands 2001: Physics Degree Extraordinary Award (First Class Honors, best GPA) 1997: Bronze Medal in the "8th Spanish Physics Olympiad" Professor Zafra has been principal investigator on numerous significant research projects including the Estonian Centre of Excellence in Artificial Intelligence, Cardiovascular Stress Impacts On Neuronal Function, and Bridging biological and artificial models of vision. His grant portfolio demonstrates strong funding support from the Estonian Research Council, European Commission, and other major funding bodies. He has supervised multiple PhD students and mentored early-career researchers in computational neuroscience and AI. His laboratory work focuses on developing computational models of neural systems and applying these insights to artificial intelligence. Current research directions include explainable AI methods, brain-computer interfaces, modeling of consciousness and cognitive processes, and the application of AI to healthcare challenges.
Tim G. J. Rudner is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, a Faculty Member at the Vector Institute, and a Title A Fellow at Trinity College, University of Cambridge. He was previously an Assistant Professor and Faculty Fellow at New York University. University: University of Toronto School: Faculty of Arts and Science Department: Department of Statistical Sciences Affiliation: Vector Institute, Trinity College (Cambridge) He holds a PhD in Computer Science and an MSc in Statistics from the University of Oxford, where he was advised by Yee Whye Teh and Yarin Gal, and a BS in Applied Mathematics and Economics from Yale University. PhD: Computer Science, University of Oxford MSc: Statistics, University of Oxford BS: Applied Mathematics and Economics, Yale University His research focuses on building robust, transparent, and trustworthy machine learning systems, particularly for high-stakes applications. He develops probabilistic models that improve generalization under distribution shifts, provide reliable uncertainty estimates, and enable fair and interpretable predictions. His work spans generative models, large language models, healthcare, and biomedical discovery. The recent publications highlight a strong trend toward function-space modeling, Bayesian regularization, and AI safety. Tim's work emphasizes principled uncertainty quantification, robustness to subpopulation and semantic shifts, and the development of frameworks for AI governance and specification. His research bridges theoretical advances with real-world applications, especially in safety-critical domains like medicine and defense. Tim has received numerous accolades including being named a Rhodes Scholar, Qualcomm Innovation Fellow, and 2024 Rising Star in Generative AI. He was awarded a $700,000 Foundational Research Grant and a $30,000 Apple Seed Grant for improving LLM trustworthiness. Rhodes Scholar Qualcomm Innovation Fellow AISTATS Notable Paper Award (2024) Outstanding Paper Award, ICLR GenAI4DM Workshop (2024) Apple Seed Grant ($30,000) Foundational Research Grant ($700,000) NeurIPS Spotlight Talk 2024 Rising Star in Generative AI He actively mentors students, particularly first-generation and low-income scholars, and has contributed to major policy frameworks including the OECD AI Classification Framework and a series of CSET issue briefs on AI safety. His work demonstrates a strong commitment to responsible AI development, combining technical rigor with societal impact. Tim leads research efforts at the intersection of machine learning theory and practical deployment, with ongoing projects in generative modeling, reliable LLMs, and AI governance. His lab produces high-impact work regularly published at top-tier conferences such as NeurIPS, ICML, and AISTATS.
Laverne Jacobs, PhD, is a Full Professor at the University of Windsor's Faculty of Law where she holds the Research Chair in Disability Equality & Administrative Justice. She serves as the founder and Director of the Law, Disability & Social Change Project, a research and public policy center dedicated to fostering inclusive communities. Notably, Professor Jacobs was elected to the United Nations Committee on the Rights of Persons with Disabilities in 2022, becoming the first Canadian to serve on this UN Human Rights Treaty Body. She also serves as Co-Director of the Disability Rights Working Group at Berkeley Law's Center for Comparative Equality & Anti-Discrimination Law and previously held the position of Associate Dean (Research & Graduate Studies) for the Faculty of Law from 2018-2021. B.A. (Honours), McGill University, 1994 LL.B., McGill University, 1999 B.C.L., McGill University, 1999 Ph.D., Osgoode Hall Law School, York University, 2009 Professor Jacobs' research sits at the intersection of disability equality law and administrative law and justice, characterized by an interest in the everyday work of the administrative justice system and the lived experiences of people who use it, particularly persons with disabilities. Her work spans disability rights, administrative justice, human rights law, socio-legal theory, and empirical research methodologies. She has made significant contributions to understanding accessibility legislation, transportation inequality, post-secondary education access for students with disabilities, and the implementation of disability equality principles in administrative decision-making. Her scholarship bridges theoretical legal frameworks with practical applications, emphasizing citizen participation in policy development and the importance of inclusive design in legal systems. Analysis of Professor Jacobs' recent publications reveals a consistent focus on how administrative law can better serve persons with disabilities through inclusive design and accessible processes. Her work demonstrates growing attention to intersectional issues, particularly how women with disabilities experience administrative systems, and increasingly examines the implementation challenges of accessibility legislation across Canadian jurisdictions. She frequently employs empirical research methods to ground her legal analysis in real-world experiences of marginalized communities. Scientific Awards and Honors: Canadian Bar Association Touchstone Award (2021) Canadian Association of Law Teachers (CALT) Academic Excellence Award (2022) Hummingbird Award from the Disabled Women's Network of Canada (DaWN), 2022 Election to the United Nations Committee on the Rights of Persons with Disabilities (2022) Professor Jacobs has held significant leadership roles including service on the Board of Directors of the Income Security Advocacy Centre and the Canadian Institute for the Administration of Justice. Through her role as Director of the Law, Disability & Social Change Project, she has secured research funding to support policy-relevant scholarship on disability rights and administrative justice. Her work has influenced accessibility legislation development across Canada, particularly through her annotated version of the Accessible Canada Act. She provides expert consultation to government bodies and international organizations on disability rights implementation. The Law, Disability & Social Change Project serves as Professor Jacobs' primary research hub, bringing together interdisciplinary scholars, disability advocates, and policy makers to develop practical solutions for creating more inclusive legal and administrative systems. The project has produced numerous research reports, policy briefs, and public resources that translate academic research into actionable recommendations for improving accessibility and disability equality in Canadian society.
Aberham Hailu Feyissa is an Associate Professor at the National Food Institute of the Technical University of Denmark , specializing in process modeling and sustainable food engineering. His research bridges complex transport phenomena with practical food manufacturing challenges. Ph.D. in Food Process Engineering, Technical University of Denmark MSc in Food Science, K.U.Leuven and Universiteit Gent BSc in Chemical Engineering, Bahir Dar University His work focuses on coupled mass and heat transfer during solid food processing, aiming to develop robust first-principles models for predictive process optimization. Key applications include Ohmic heating , digital twin technology , and bioactive ingredient extraction . Recent publications highlight his contributions to sustainable food processes , including insect-based feed modeling , seaweed bread kinetics , and clean-label cheese formulation . His methodological innovations span FTIR spectroscopy , computational fluid dynamics , and kinetic modeling . Supervisor for PhD students S. S. Turgut , M. E. Jabali , and Dahal S. Principal Investigator for projects like Modelling and Digitalisation of Food Processes and Sustainable Extraction of Bioactive Insect Fractions Active in conference presentations and peer review , he drives advancements in food process understanding through mechanistic modeling and mathematical simulation .
Deian Stefan is an Associate Professor at the University of California San Diego (UCSD) in the Department of Computer Science and Engineering . His research spans security , programming languages , and systems , with a focus on building principled and practical secure systems. He has served as a co-founder and Chief Scientist at Intrinsic (acquired by VMWare) and contributed to standards bodies like the W3C WebAppSec and Node.js Security Working Groups . His research interests include: Secure Systems : Web frameworks, browser designs, sandboxing, runtime systems Language-Based Security : Constant-time programming, memory safety, information flow control Verification : Security verification, static/symbolic analysis tools WebAssembly and JavaScript JITs security Deian Stefan has received multiple scientific awards , including several Distinguished Paper Awards at venues like POPL, ICFP, and USENIX Security, as well as the IEEE Cybersecurity Award for Practice (2022) and CSAW 2020 First Place for Applied Research. He has taught courses on Computer Security (CSE 127, CSE 227) and advanced topics in Building Secure Systems (CSE 291) using Rust, WebAssembly, and blockchain security. His work has been supported by collaborations with industry and academia, including projects like RLBox and COWL .
Dr. Adriana Bocchini is a Researcher at the University of Paderborn , affiliated with the Theoretical Materials Physics department and the Quantum Materials Modelling group. Her work focuses on computational modeling of materials, particularly crystal defects, surface adsorption, and electrochemical properties using advanced theoretical methods. Research Interests: Adriana's research spans Theoretical Materials Physics and Quantum Materials Modelling , with a focus on Defect modeling in ferroelectric materials Surface adsorption mechanisms Electronic structure calculations First-principles simulations Recent Publications: She has contributed to studies on radiation-induced defects in KTiOPO 4 , Mg doping effects in lithium niobate, phosphonic acid interactions with bismuth oxide, and electrochemical properties of doped RTP crystals, all leveraging computational approaches like Density Functional Theory (DFT). Labs & Teams: Adriana is actively involved in the Theoretical Materials Physics and Quantum Materials Modelling groups at the University of Paderborn, advancing computational studies in materials science.
Paul D. Asimow is the Eleanor and John R. McMillan Professor of Geology and Geochemistry at the California Institute of Technology (Caltech), part of the Division of Geological and Planetary Sciences. He holds a B.A. from Harvard University (1991), an M.S. (1993), and a Ph.D. (1997) from Caltech. His career progression includes roles as Assistant Professor (1999–2005), Associate Professor (2005–2010), and Professor (2010–present), with the McMillan Professorship since 2016. Education: A.B. in Geology, Harvard University, 1991 M.S. in Geology, Caltech, 1993 Ph.D. in Geology, Caltech, 1997 Research Interests: Focuses on computational, experimental, and observational approaches to igneous petrology and mineral physics. Key areas include adiabatic mantle melting, water's role in mantle dynamics, high-pressure mineral physics, and processes at mid-ocean ridges. His research utilizes advanced facilities like the Lindhurst Laboratory of Experimental Geophysics and the alphaMELTS software package for thermodynamic modeling. Articles Overview: Recent work spans planetary crust formation, Martian petrogenesis, and high-pressure mineral behavior. Themes include experimental techniques, computational modeling, and cosmochemical studies of meteorites. Awards and Honors: James B. Macelwane Medal (AGU) Frank Wigglesworth Clarke Medal (Geochemical Society) Richard P. Feynman Prize for Teaching Excellence (Caltech) Fellow of the American Geophysical Union Fellow of the Mineralogical Society of America Grants and Labs: Received NSF funding for developing an interactive phase equilibria curriculum. Leads the Lindhurst Laboratory, focusing on shock-wave experiments and high-pressure mineral physics. Collaborates on software tools like alphaMELTS and MAGMASOURCE. Labs and Teams: Active in the Caltech Shock Wave Laboratory, advancing experimental methods for planetary material studies. Engages in interdisciplinary projects on Mars geology and terrestrial planet formation.
Bryan H. Choi is an Associate Professor of Law at the University of Colorado Law School , where he bridges law and computer science to address software and AI safety. His work on software liability has influenced national cybersecurity strategy discussions. As an Adviser for the ALI Principles Project on Civil Liability for Artificial Intelligence , he shapes legal frameworks for emerging technologies. Education : JD and AB in Computer Science from Harvard University; clerkships with U.S. Court of Appeals judges Leonard I. Garth and William C. Bryson. Roles : Former joint appointment at Ohio State University Law School and Computer Science Department; Faculty Fellow at UPenn's CTIC; Director of Law and Media at Yale's ISP. Research Focus : Choi's scholarship examines software liability , AI accountability , and privacy law through interdisciplinary lenses. He critiques institutional approaches to software safety and advocates for empirical legal frameworks over participation-based models. Recent Articles address AI malpractice , NIST software standards , and forensic tool validation , reflecting trends in AI regulation and cyber-physical system liability . His 2021 NSF grant funded technical-legal methods for safety-critical systems. Awards & Grants : National Science Foundation (NSF) Grant (2021) Adviser, ALI Principles Project on Civil Liability for Artificial Intelligence Community Engagement : Active in Law and Computer Science communities , serving on committees for the ACM Symposium , Cybersecurity Law and Policy Scholars Conference , and co-organizing the AAAI Bridge Program on AI and Law .
Associate Professor Quek Su Ying is affiliated with the Department of Physics at the National University of Singapore (NUS) and serves as Assistant Dean (Special Duties). Her research focuses on theoretical and computational approaches to understanding the electronic, vibrational, and transport properties of emerging materials, particularly 2D and organic systems. Affiliations : Institute of High Performance Computing, Centre for Advanced 2D Materials, NUS Research Interests : First principles calculations (mean field and many-electron perturbation theory), interface science, electronic energy level alignment, and transport in emerging materials. Her work includes studies of exciton condensation, quantum emitters, and valleytronic control via magnetic fields. Article Trends : Recent publications highlight investigations into 2D materials, organic-inorganic interfaces, and quantum phenomena. Topics include exciton dynamics, defect engineering, charge density waves, and spin-dependent transport, employing advanced ab initio methods like GW theory. Scientific Awards : Singapore NRF Fellowship Advising & Collaborations : Her group develops state-of-the-art computational methods and collaborates with experimental teams. Notable affiliations include Google Scholar Profile and partnerships with institutions like the Institute of High Performance Computing. Labs & Teams : Associated with the Centre for Advanced 2D Materials at NUS, which supports interdisciplinary research on graphene and related 2D systems. Her work bridges theoretical modeling and experimental validation.
Roya Ensafi is the Morris Wellman Associate Professor in the Department of Computer Science & Engineering at the University of Michigan. She is the Founder and Director of the Censored Planet Lab, which focuses on Internet censorship measurement and digital equity. Her research lies at the intersection of networking, security, and privacy, with a strong emphasis on detecting censorship, surveillance, and digital inequity through scalable systems. Positions: Associate Professor (University of Michigan), Lab Director (Censored Planet) Recent Awards: Sloan Research Fellowship, NSF CAREER (2023), IRTF Applied Networking Research Prize (2016, 2022, 2023), USENIX Security Internet Defense Prize (2022) Her work develops systems like Censored Planet for global censorship monitoring, VPNalyzer for evaluating commercial VPN security, and Splintering Net for studying regionalized Internet access. By combining remote measurement techniques with user studies, her research addresses both technical and policy dimensions of digital freedom. Key methodologies include TLS handshake analysis, cross-layer latency metrics, and large-scale network probing. The Censored Planet project operates a global censorship detection network covering 221 countries, while VPNalyzer received the Consumer Reports Digital Lab fellowship. Collaborations include Google Jigsaw for data visualization systems used by over 100 organizations. Her 2024 work on digital discrimination in sanctioned states extends earlier groundbreaking research on Kazakhstan's HTTPS interception (2019) and Russia's Twitter throttling (2021). Scientific Awards: 2024: Distinguished Paper Awards at USENIX Security Symposium 2023: NSF CAREER Award 2022: IRTF Applied Networking Research Prize, USENIX Security Internet Defense Prize, First Prize in Internet Defense Prize, CSAW '22 Applied Research Competition First Place 2021: Recognized as Highest Scoring Short Paper at ACM IMC 2015: IRTF Applied Networking Research Prize 2022: Finalist for ACUM Outstanding Advisor Award Her lab trains both current and alumni PhD/Master's students including Ram Sundara Raman, Diwen Xue, Reethika Ramesh (now at Palo Alto Networks), and Victor Ongkowijaya (PhD at Princeton). She teaches EECS 388 Introduction to Security at the University of Michigan, covering software and network security principles. Her work has been featured in The Economist, New York Times, and BBC for analyzing global censorship trends.
Andrew Ollett is an Associate Professor and Director of Graduate Studies in the Department of South Asian Languages and Civilizations at the University of Chicago , where he has taught since 2019. His research focuses on the literary and intellectual traditions of South and Southeast Asia, particularly through works in Sanskrit, Prakrit, Apabhramsha, and Kannada from the first millennium CE. Co-founder and editor of NESAR (New Explorations in South Asia Research) Director of NEH-funded edition/translation of the Kavirājamārga (earliest Kannada literary manual) Expert in Prakrit and Kannada literary traditions His work explores the 'question of language'—examining how language choice shaped cultural production and change in premodern India. Current projects include a monograph on context-sensitivity in Indian language theories and a study of manuscript technology's impact on early South Asian knowledge communities. He has authored The Mirror of Ornaments (Naples: Unior Press, 2025) and Language of the Snakes (UC Press, 2017), and co-authored Lilavai (Harvard, 2021). Recent publications include interdisciplinary studies on: Sanskrit-Prakrit campū genre reevaluation Pragmatics in Mīmāṁsā interpretive concepts Manuscript literacy's influence on literary forms Historical linguistics of Middle Indic syllable reduction Philosophy of language in Śālikanātha and Kumārila Bhaṭṭa Awarded the NEH 'Scholarly Editions and Translations' grant , he teaches classical South Asian literature courses covering Sanskrit, Prakrit, and Tamil traditions. He maintains digital resources including a Prakrit Digital Texts Project and Mīmāṁsādigdarśinī digital text repository, and actively contributes to open-access scholarship.
Dr. Christopher M. Wolverton is a Professor of Materials Science and Engineering at Northwestern University , where he leads the Wolverton Research Group . His work focuses on computational materials science with applications in energy sustainability , particularly in batteries , hydrogen storage , and thermoelectrics . PhD in Physics from University of California, Berkeley BS in Physics (summa cum laude) from University of Texas, Austin His research leverages first-principles quantum mechanical simulations and machine learning to enable virtual materials synthesis before laboratory testing. The group specializes in hybrid computational methods integrating Density Functional Theory (DFT) , Monte Carlo simulations , and phase-field microstructural models . The article portfolio shows leadership in energy storage materials , with recent work on data-driven nanoparticle facet control , mixed-anion semiconductors , and machine learning-accelerated discovery . Publications span top journals including Nature Energy , Nature Materials , and Science . 2006 Ford Motor Company Technical Achievement Award 2005 Ford Patent & Publication Awards 2003 Ford Environmental/Physical Sciences Recognition As advisor to PhD candidates Zhenpeng Yao , Shiqiang Hao , and Shane Patel , he fosters interdisciplinary research connecting materials informatics with experimental validation . The group maintains active collaborations with Argonne National Lab and MIT/Harvard teams.
Dane Morgan is a Professor in the Department of Materials Science & Engineering at the University of Wisconsin-Madison, College of Engineering. His research focuses on computational materials science for materials design, including ab initio electronic structure modeling, multiscale methods, and machine learning applications in materials discovery. His work spans nuclear materials, battery and fuel cell electrodes, and electronic materials. Education : PhD, 1998, University of California, Berkeley MS, 1994, University of California, Berkeley BA, 1992, Swarthmore College Research Interests : Computational materials science, ab initio methods for electronic structure and thermokinetics, machine learning for materials discovery, electrochemical systems modeling, and applications in nuclear materials, batteries, and electronic materials. His work integrates advanced computational techniques with experimental validation. Scientific Awards : 2024 APL Materials, Editors Pick 2023 Microscopy and Microanalysis Best Paper Award (Instrumentation and Software category) 2023 IEEE Transactions on Plasma Science Best Paper Award 2023 Kellet Mid-Career Award 2015 TMS Materials Genome Initiative Ambassador 2006 3M Technical Nontenured Faculty Grant
Scott Hopkins is a Professor in the Department of Chemistry at the University of Waterloo, specializing in Physical Chemistry. His research integrates machine learning with experimental techniques to study ion mobility, mass spectrometry, and spectroscopic analysis. He directs the Hopkins Laboratory, focusing on computational predictions of chemical behaviors and molecular interactions. His work addresses fundamental questions in gas-phase chemistry, cluster formation, and analytical method development. Research interests span physical chemistry, computational modeling, and analytical instrumentation, with a strong emphasis on developing predictive tools for complex chemical systems. Recent investigations explore ion-solvent dynamics, fragmentation mechanisms, and machine-learning applications for spectral interpretation.
